{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "464b32d5-56c1-4ada-a8ad-7704129dff91",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This ipynb is to HD-analyze OH legisl with OH county splits\n",
      "In version 1, we ignored municipality splits.  Here, we post-snap to munis, or separately vtds\n",
      "I read in the Census 2020 pop data -- mapped onto 2020 vtds\n"
     ]
    }
   ],
   "source": [
    "print(\"This ipynb is to HD-analyze OH legisl with OH county splits\") #v1 (legisl_byCounty) completed Nov'22\n",
    "print(\"In version 1, we ignored municipality splits.  Here, we post-snap to munis, or separately vtds\")\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "import ast\n",
    "pi = math.pi\n",
    "tractPopFile = gpd.read_file(\"state_map_files/oh_pl2020_vtd.dbf\") #**CHANGE FILENAME AS NEEDED*****\n",
    "tractPopFile.head() \n",
    "print(\"I read in the Census 2020 pop data -- mapped onto 2020 vtds\")\n",
    "dummyPoly = Polygon([(0,0),(0,1),(1,1)])#\n",
    "#tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fcba8000-5fa0-48ce-ba34-bfdf195594a5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#handy basic functions for Home District analysis\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.geom_type == simplePoly.geom_type:  #in future, change this to .geom_type *********************\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            if geom.area > 0:  #to avoid error with LineString geom parts\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)         \n",
    "def plotCenter(t,geom):\n",
    "    plt.text(geom.centroid.x,geom.centroid.y,t,ha='center')\n",
    "    \n",
    "def r3(number):\n",
    "    result = round(number,3)\n",
    "    return result\n",
    "\n",
    "def r5(number):\n",
    "    result = round(number,5)\n",
    "    return result\n",
    "\n",
    "def makePiePoly(CENTER,angle,maxR):  #this is part of splitting 2-district counties; making a PIE-SLICE polygon that should capture a pie slice's tracts\n",
    "    gCx = CENTER.x\n",
    "    gCy = CENTER.y\n",
    "    pt0 = Point(gCx + maxR*math.cos(angle), gCy + maxR*math.sin(angle))\n",
    "    pt1 = Point(gCx - maxR*math.cos(angle), gCy - maxR*math.sin(angle))\n",
    "    pt2 = Point(pt1.x + maxR*math.sin(angle) , pt1.y - maxR*math.cos(angle))\n",
    "    pt3 = Point(pt0.x + maxR*math.sin(angle) , pt0.y - maxR*math.cos(angle))\n",
    "    piePoly = Polygon([pt0,pt1,pt2,pt3])\n",
    "    return piePoly\n",
    "\n",
    "def makePentaPoly(CENTERPT, DX, DY, LENGTH):\n",
    "    \"\"\"\n",
    "    This draws a pentagon with one edge as the snapping edge, other four big enough to capture all pop on one side of main edge\n",
    "    This is used in piecut2 code to find the edge position (at a fixed angle) to cleave a district's pop from a tract list\n",
    "    DX and DY are relative values to draw the leading edge centered by the centerpoint, and two perp'lar lines away from the edge\n",
    "    \"\"\"\n",
    "    lengthRatio = LENGTH / (DX*DX + DY*DY)**0.5\n",
    "    DXR = DY*lengthRatio\n",
    "    DYR = -1.*DX*lengthRatio\n",
    "    pt0 = Point(CENTERPT.x + DXR, CENTERPT.y + DYR)\n",
    "    pt1 = Point(CENTERPT.x - DXR, CENTERPT.y - DYR)\n",
    "    pt2 = Point(pt1.x - DYR, pt1.y + DXR)\n",
    "    pt3 = Point(pt0.x - DYR, pt0.y + DXR)\n",
    "    farPt = Point(0.5*(pt2.x + pt3.x) + 0.1*DYR, 0.5*(pt2.y + pt3.y) + 0.1*DXR)  #a bump to clarify which is far edge\n",
    "    pentaPoly = Polygon([pt0, pt3, farPt, pt2, pt1])\n",
    "    return pentaPoly\n",
    " \n",
    "def angledPentaPoly(CENTERPT, ANGLE, LENGTH):\n",
    "    \"\"\"\n",
    "    This draws a pentagon with one edge as the snapping edge, other four big enough to capture all pop on one side of main edge\n",
    "    ANGLE is the \"pizza-cutting\" angle through the centerpoint to divide a region into \"included\" and \"excluded\" sub-regions\n",
    "    The pentaPoly of specified (large) LENGTH will capture all \"included\" points after we pass it back to the main code.\n",
    "    DX and DY are relative values to draw the leading edge centered by the centerpoint and two perp'lar lines away from the edge\n",
    "    \"\"\"\n",
    "    DX = 3.*math.cos(ANGLE)*LENGTH   #\"3\" gives some buffer to ensure pentaPoly captures all on one side\n",
    "    DY = 3.*math.sin(ANGLE)*LENGTH\n",
    "    pt0 = Point(CENTERPT.x + DX, CENTERPT.y + DY)\n",
    "    pt1 = Point(CENTERPT.x - DX, CENTERPT.y - DY)\n",
    "    pt2 = Point(pt1.x - DY, pt1.y + DX)\n",
    "    pt3 = Point(pt0.x - DY, pt0.y + DX)\n",
    "    farPt = Point(0.5*(pt2.x + pt3.x), 0.5*(pt2.y + pt3.y) )\n",
    "    farPt = Point(1.1*farPt.x - 0.1*CENTERPT.x, 1.1*farPt.y - 0.1*CENTERPT.y )   #a bump-out to clarify which is far edge\n",
    "    pentaPoly = Polygon([pt0, pt3, farPt, pt2, pt1])\n",
    "    return pentaPoly  \n",
    "\n",
    "def get_PCP(TRACTLIST, TRACTCP, TRACTPOP):  \n",
    "    \"\"\"\n",
    "    function to get population-weighted centerpoint of a group of tracts (e.g. an HDtractList or county)\n",
    "    the TRACTLIST is the subset of tracts (vtds) of interest\n",
    "    TRACTCP, TRACTPOP are global lists of tract centerpoints, pops\n",
    "    \"\"\"\n",
    "    sumPop = 0.\n",
    "    PCPx = 0.\n",
    "    PCPy = 0.\n",
    "    for TT in TRACTLIST:\n",
    "        sumPop += TRACTPOP[TT]\n",
    "        PCPx += TRACTCP[TT].x * TRACTPOP[TT]\n",
    "        PCPy += TRACTCP[TT].y * TRACTPOP[TT]\n",
    "    PCPx = PCPx / sumPop\n",
    "    PCPy = PCPy / sumPop\n",
    "    return Point(PCPx, PCPy)\n",
    "\n",
    "#def debug_pieCut2(T,TARGETSLICEPOP, TOLERPOP, TRACTLIST, TRACTCP, TRACTPOP, origANGLE) :  #OBSOLETE; see a prev file\n",
    "# see later blocks for legislCountyMuni-specific methods\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ae933c8a-74cf-491c-9e01-f4d11e14bbe4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 8941 vtd tracts for OH using Census vtds\n",
      "OH has 88 counties and a total population of 11799448\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "STATE = \"OH\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractNAME20 = tractPopFile['NAME20']\n",
    "tractGEOID = tractPopFile['GEOID20']  #needed to translate DRA-based precinct assignment files\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #USE VAP.  Can use ACS CVAP if more accurate data needed\n",
    "tractHisp = tractPopFile['P0040002']   #USE VAP.  Can use ACS CVAP if more accurate data needed\n",
    "tractBlack = tractPopFile['P0030004']  #USE VAP.  Can use ACS CVAP if more accurate data needed\n",
    "tractCountyNo = tractPopFile['COUNTYFP20']\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} vtd tracts for {1} using {2}\".format(nTracts, STATE,\"Census vtds\") )\n",
    "tractArea = [0.]*nTracts\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractCP = [Point(0,0)]*nTracts\n",
    "tractCountyNo = tractCountyNo.to_numpy()\n",
    "countyNo = [0]*nTracts  #the tract's county number\n",
    "tractNo = [i for i in range(nTracts)]  #original row number in tractPopFile\n",
    "for t in range (0,nTracts) :  #from odd-numbered counties in US Census list to integer list\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "    countyNo[t] = int((int(tractCountyNo[t]) - 1)/2)\n",
    "    tractCP[t] = Point(tractGeom[t].centroid)\n",
    "    tractCPx[t] = tractCP[t].x\n",
    "    tractCPy[t] = tractCP[t].y\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)\n",
    "nCounties = int( np.max(countyNo)+1)\n",
    "statePop = np.sum(tractPop)\n",
    "print(STATE,\"has\",nCounties,\"counties and a total population of\",statePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6344566b-282a-4b8e-a174-4369489c9274",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now read in the 2020 voting data from a separate file\n",
      "I have read in the 2020 voting data for all 8941 precincts from 88 counties\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now read in the 2020 voting data from a separate file\")\n",
    "VTDdbf = gpd.read_file(\"state_map_files/oh_vest_20.dbf\")\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "vtd_Trump = VTDdbf['G20PRERTRU']\n",
    "vtd_Biden = VTDdbf['G20PREDBID']\n",
    "vtd_GEOID20 = VTDdbf['GEOID20']\n",
    "vtd_Trump = vtd_Trump.to_numpy()\n",
    "vtd_Biden = vtd_Biden.to_numpy()\n",
    "vtd_NAME20 = VTDdbf[\"NAME20\"] #official full name of precinct\n",
    "nPrecincts = len(vtd_NAME20)\n",
    "pMuniName = [\"unassigned\"]*nPrecincts  #could be used later to build municipalities out of similarly named precincts\n",
    "VTDCountyNo = VTDdbf['COUNTYFP20']\n",
    "VTDCountyNo = VTDCountyNo.to_numpy()\n",
    "pCountyNo = [0]*nPrecincts\n",
    "vtd_CPx = [0.]*nPrecincts  #these two are the x- and y-coordinates of the centroid of each vtd shape\n",
    "vtd_CPy = [0.]*nPrecincts\n",
    "vtd_Area = [0.]*nPrecincts\n",
    "for p in range (0,nPrecincts) :  #from odd-numbered counties in US Census list to integer list\n",
    "    pCountyNo[p] = int((int(VTDCountyNo[p]) - 1)/2)\n",
    "    vtd_CPx[p] = vtdGeom[p].centroid.x\n",
    "    vtd_CPy[p] = vtdGeom[p].centroid.y\n",
    "    vtd_Area[p]= vtdGeom[p].area\n",
    "print(\"I have read in the 2020 voting data for all\",nPrecincts,\"precincts from\",np.max(pCountyNo)+1,\"counties\")\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "49d970c6-4bb4-4a62-a858-1c847776546e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now create a dataframe with the voting data, then merge it with the vtd tract demographic data\n",
      "building a list of names of tracts for quick lookup\n"
     ]
    }
   ],
   "source": [
    "vtdNo = [0]*nPrecincts\n",
    "orig_vtd_GEOID20 = vtd_GEOID20 #for safekeeping\n",
    "for p in range(nPrecincts):\n",
    "    vtdNo[p] = p  #tracking the original vtdNo so we can connect to original geometry\n",
    "print(\"I will now create a dataframe with the voting data, then merge it with the vtd tract demographic data\")\n",
    "vtdDF = pd.DataFrame( {\"vtdBiden\":vtd_Biden,\"vtdTrump\":vtd_Trump,\"vtdCPx\":vtd_CPx,\"vtdCPy\":vtd_CPy,\"pNAME20\":vtd_NAME20,\n",
    "                       \"GEOID20\":vtd_GEOID20,\"vtdArea\":vtd_Area, \"vtdNo\":vtdNo } ) \n",
    "mergedDF = pd.merge(tractPopFile,vtdDF,on=\"GEOID20\")\n",
    "vtdBiden = mergedDF['vtdBiden'].to_numpy()\n",
    "vtdTrump = mergedDF['vtdTrump'].to_numpy()\n",
    "vtdCPx = mergedDF['vtdCPx'].to_numpy()\n",
    "vtdCPy = mergedDF['vtdCPy'].to_numpy()\n",
    "vtdCP = [Point(0,0)]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdCP[p] = Point(vtdCPx[p],vtdCPy[p])   #in this context, vtd centerpoints are near-equivalent to tract centerpoints\n",
    "vtdArea = mergedDF['vtdArea'].to_numpy()\n",
    "pNAME20 = mergedDF['pNAME20']\n",
    "GEOID20 = tractPopFile['GEOID20']\n",
    "old_vtdNo = mergedDF['vtdNo']\n",
    "print(\"building a list of names of tracts for quick lookup\")\n",
    "tractNAME20List = []\n",
    "for t in range(nTracts):\n",
    "    tractNAME20List.append(tractNAME20[t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "03096495-6395-46b6-bfa7-e11e1a9666d4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I moved Put-in-Bay on top of Sandusky.  I will now finalize the tract center locations.\n",
      "lets visualize the unpopulated precincts and build the list of populated precincts = tracts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will skip tracts [840, 1008, 2974, 3730, 4458, 4634, 8197] in building the county-by-county map of OH\n"
     ]
    }
   ],
   "source": [
    "#special N. island precinct -- move it to Sandusky (closest vtd in same county)\n",
    "PIB = 3739\n",
    "Sand = 3622\n",
    "tractGeom[PIB] = tractGeom[Sand]\n",
    "vtdCPx[PIB] = vtdCPx[Sand]\n",
    "vtdCPy[PIB] = vtdCPy[Sand]\n",
    "vtdArea[PIB] = vtdArea[Sand]\n",
    "tractCP = [Point(0,0)]*nTracts\n",
    "print(\"I moved Put-in-Bay on top of Sandusky.  I will now finalize the tract center locations.\")\n",
    "for t in range(nTracts):  #need to update center points now that I've moved at least one\n",
    "    tractCP[t] = tractGeom[t].centroid\n",
    "    tractCPx[t] = tractCP[t].x\n",
    "    tractCPy[t] = tractCP[t].y\n",
    "vtdCP = [Point(0,0)]*nPrecincts  #update vtd center points after any moves above\n",
    "for p in range(nPrecincts):\n",
    "    vtdCP[p] = Point(vtdCPx[p],vtdCPy[p])   #in this context, vtd centerpoints are near-equivalent to tract centerpoints\n",
    "\n",
    "minTractPop = 10\n",
    "skipList = []\n",
    "populatedTractList = []\n",
    "print(\"lets visualize the unpopulated precincts and build the list of populated precincts = tracts\")\n",
    "for p in range(nPrecincts):\n",
    "    if tractPop[p] < minTractPop and vtdCPy[p] > 41.0:  #lake Erie only; ignore interior lake tracts\n",
    "        plotPoly(tractGeom[p])\n",
    "        plotCenter(p,tractGeom[p])\n",
    "        skipList.append(p)\n",
    "    else:\n",
    "        populatedTractList.append(p)\n",
    "plt.show()\n",
    "print(\"we will skip tracts\",skipList,\"in building the county-by-county map of\",STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6d9e0115-02f6-4457-9058-722a7e7048db",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have pushed island precincts into the map and eliminated lakeshore tracts. Now build the county-level map\n",
      "working on tract 0\n",
      "working on tract 1000\n",
      "working on tract 2000\n",
      "working on tract 3000\n",
      "working on tract 4000\n",
      "working on tract 5000\n",
      "working on tract 6000\n",
      "working on tract 7000\n",
      "working on tract 8000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "the total of all county pops is 11799448.0 vs a state pop of 11799448\n"
     ]
    }
   ],
   "source": [
    "print(\"We have pushed island precincts into the map and eliminated lakeshore tracts. Now build the county-level map\")\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1)])\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "countyPop = [0.]*nCounties\n",
    "nCountyTracts = [0]*nCounties  #how many tracts found in this county\n",
    "countyTractList = [\"empty\"]*nCounties  #list of this county's tracts\n",
    "for t in range(nTracts):  #now loop through tracts, assign each to county, build county polygons\n",
    "    if t%1000 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    c = int(countyNo[t])\n",
    "    nCountyTracts[c] +=1   #found another tract that belongs to this county (we include skipped tracts here)\n",
    "    countyPop[c] += tractPop[t]\n",
    "    if t not in skipList:\n",
    "        if countyGeom[c] == dummyPoly:  #this is the first tract found for this county\n",
    "            countyGeom[c] = tractGeom[t]  #seed the county geometry polygon\n",
    "            countyTractList[c] = [t]\n",
    "        else:\n",
    "            countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "            countyTractList[c].append(t)\n",
    "countyMAP = []\n",
    "for c in range(nCounties):  #now build the state map from the counties\n",
    "    plotPoly(countyGeom[c])\n",
    "    if countyMAP == []:  #is this the first county we are adding to map?\n",
    "        countyMAP = countyGeom[c] \n",
    "    else:\n",
    "        countyMAP = countyMAP.union(countyGeom[c])\n",
    "plotPoly(countyMAP)\n",
    "for c in range(nCounties):\n",
    "    plotCenter(c,countyGeom[c])   \n",
    "plt.show()\n",
    "print(\"the total of all county pops is\",np.sum(countyPop),\"vs a state pop of\",statePop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5e6fe400-5d6a-4281-a1e4-c2ee3ebcd4a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Let us visualize the county pops relative to a district pop and their 2020 R-D leans\n",
      "OH has 99 districts, each with an avg pop of 119186.34343\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Let us visualize the county pops relative to a district pop and their 2020 R-D leans\")\n",
    "nDistricts = 99\n",
    "statePop = np.sum(tractPop)\n",
    "avgDistrictPop = statePop / float(nDistricts)\n",
    "aDP = avgDistrictPop\n",
    "print(STATE,\"has\",nDistricts,\"districts, each with an avg pop of\",r5(avgDistrictPop))\n",
    "countyDistricts = [0.]*nCounties\n",
    "county_vGOP = [0.]*nCounties\n",
    "countyVoters = [0.]*nCounties\n",
    "for c in range(nCounties):\n",
    "    countyDistricts[c] = countyPop[c] / avgDistrictPop\n",
    "    plotPoly(countyGeom[c])\n",
    "    countyTrumpers = 0.\n",
    "    for t in countyTractList[c]:\n",
    "        countyTrumpers += vtdTrump[t]\n",
    "        countyVoters[c] += vtdTrump[t] + vtdBiden[t]\n",
    "    county_vGOP[c] = countyTrumpers / countyVoters[c]\n",
    "    if countyDistricts[c] > 0.2:  #skip the tiny counties\n",
    "        COLOR = \"red\"\n",
    "        if county_vGOP[c] < 0.57:\n",
    "            COLOR = \"purple\"\n",
    "            if county_vGOP[c] < 0.43:\n",
    "                COLOR = \"blue\"\n",
    "        plt.text(countyGeom[c].centroid.x - 0.2, countyGeom[c].centroid.y,round(countyDistricts[c],2),color=COLOR)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5bd82dc8-949d-4ba0-8fbe-d42738878d02",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets see if countyMAP is a multipolygon.  If so, we will take the biggest piece as the true MAP\n",
      "by golly, it is!  I'll now plot the main piece of the map\n"
     ]
    },
    {
     "data": {
      "image/png": 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V1XaOn5xY6/Apc8LMXZtCbKgPNw6N45aR8fh46aIgzWvRzgxmff4DeSdnqIWaNX9+c0kXxnRrh1nTMshPKLA4waIWFmkBQv28mNg7komnLLRYUFrFwexiThRXUF5lo7TSxpYjeXyz4zhpuWU8u2gv761N4c3pgzRcWppFUXkVT/5vD59uTgNqJk+87aJ4Lu7WTn2w5JwUWJygYc3SUgX5ejIoru5Mub8c2pEnpvbhfz8c46XvDpCeV8atczfx5T2jiA1tnL4CBaVVvLnyEN/szKCovJr7J3Rj2rC4Rjm2tFxrDp7gj//5gaP5ZZhMcOfozvx+Qg+8PDTKTeqnwOKEH4c1u7gQkUbi42Xh2sGxTOoTyWUvruJofhk3vrOelX+45IL7tZRUVHP1a2vq3IL60xc7CfOzMrlP5DleKa1VWaWNp7/ewwfrjwAQG+rDc7/ox/DOYS6uTJzlDpc/xVonmE/+L9mVWKSVCfT25NNfDwcgLbeM+z5NoqSi+oKOmVlY7ggrI7uE0Tc6CMOAuz7cwlNf7aaqhS3SKRemsLyKXn9e5AgrNw3vyKKZoxVWWihXdtRXYHGC+rBIaxYT4svTV/fFbIIvk44x5eVVLNqZgXGe3++d2/k7Pg73tzL/NyO5ekA0AG+vSubXH2yh+iehpcpmp7C8CmkdDMMg+UQJH6xLIfHxxY7tY3u048mr+uJnVeO+NJy+a5xg0i0haeVuHNaRru39ufeTbaTklHLXh1voEx3InaO7cGVihwb9VZWUlu/4eGyPdnh5mPn79f2Z1DuS+z7dxvd7s/jl2+tJjAnmaF4ZB7KKOJRd0yIT7OtJr8hARnUN49djuuCpGXxbjJKKalbsz2bxrgxWHThx2srl94/vzr3jurqoOmkNLui3wZw5czCZTNx3332ObZ9//jmTJk0iPDwck8lEUlJSvceZO3cuJpPptEd5efmFlNdoNHGctAVDO4Wy+IHR/PaSrvh4Wth5tJB7P9nGxL+vZN7GVEorz32ryG43+HD9Ea5/cx0AMSE+/KxflOP5yX0i+ccvB+JlMbMpJY9/rk5m0a4MR1gByC+tYt3hHJ5fvJ+/LtrbNG9UGtXh7GIe+3InQ59ayt0fbeWLpGN1wsr947uz94nJzBzfTfP+yAU57xaWTZs28dZbb5GYWHdRt5KSEkaNGsW1117Lr371K6ePFxgYyL59++ps8/b2Pt/yGtWP87AosEjrFujtyf9N6sFtF3XikQU7+G5vFgeyinno8x089dUehnQKJTEmiJ6RgXRp50e13SA9r4y9xwv5ZmfN5F9QM0PpP28efNoaRxMSIlj+h7H8e3M6pVXVBPl4khgdTFyYL/5WD1JzS7nuzXVUVNvZdazQFf8FLYJhGOSXVuHjZcHqYXZJECipqObvS/bz7toUx4RvHUN9mdwnkvG9IkiICsTDbMLbU3P8tApucPk7r8BSXFzMtGnTePvtt3nyySfrPDd9+nQAUlJSGnRMk8lEZKR7jiDQWkLS1oT6efH6TYPILank35vT+GhDKqm5pXy/N4vv92ad9XX+Vg/+b2J3po+Id/zc1CqpqOabnRlUVNsYEh9CkK8noX5eTHtnA4dPaWWpNbKLOmXWWrQzg93HCvD2srD2YA4/pOdTWF7T4mX1MBMZ5E2/mGAqq+1c2S+KS3u2b7LJAKttdr7acZxnv9nLsZOTEl7Sox23X9RZK4i3Aa48u+cVWO655x6mTJnC+PHjTwss56u4uJi4uDhsNhv9+/fniSeeYMCAAWfdv6KigoqKCsfnhYVN99eY5mGRtirUz4tfj+nCry7uTFJ6Pj+k5bP2UA6Ld2fiYTYR4O1BTIgvcWG+DOscxuTekbQLsLLuUA7zt6bzZdJRqmwGD1/ek6e/btgtnrvGdGmid+X+sosq2Jaax7a0fF5ffuic+1ZU2zmSU8qRnFIAFu3KwMfTwkXdwukZGUB8mB/x4X50CvcjxNezTqAoq7SRklNCyokSknNKyCwop6iimvIqGx5mMx2CvenSzp8u7fypstlZeyiHL7YdJTW35mvFhvrwxNQ+jO3Rvun+M0ROanBgmTdvHlu3bmXTpk2NVkTPnj2ZO3cuffv2pbCwkJdeeolRo0axfft2unXrdsbXzJkzh9mzZzdaDedSe0tIeUXaKrPZRKC3J7kllXRt70+X9v7Y7QY2u0G13cBuGOzLKGTrkTy2p+XXmYMFOC2sdAr3IzW3FJvdoF9MENvTC077mp9vPcobKw/RpZ0/r00b2Ko74GYWlrP+cA4bknNZfzjnjC1OAMM7hzK5dySD40Pp2r4mRBzKLmHdoRyW7c1iY0ouQT6eFJRVsWR3Jkt2Z9Z5faC3B53C/fD2tHAkp5SMwvPrJxji68ltozpxx8WdtayDNJsGBZa0tDRmzpzJ4sWLG7V/yfDhwxk+fLjj81GjRjFw4ED+8Y9/8PLLL5/xNbNmzeKBBx5wfF5YWEhsbGyj1XSq2sBiU2KRNmpHegG/eGMtFdXOzaHiaTFx7eBYPt6QWmf7qj9e4phNt7C8iuLyaqKCfbjl3Y0s35ddZ98/zv8BgMPZJdw2dxP/vHlIi58R1TAMXl12kNySKobEh7AxJZc1B0+wP7PugpUmE3Rr78+A2BAGxYXQq0MgfWOCTjuet6eF/rHB9I8N5jdjuzi+xq5jhaw7lENyTgnJ2SWk5JRwvKCcwvLq08JhkI9nTQtMmC/RIT4Eenvi42WhstpOWm4ph7JLOJRdjN0wGNopjIu6hnFlvyh8vTTIVJpXg77jtmzZQlZWFoMGDXJss9lsrFy5kldeeYWKigoslgtP22azmSFDhnDgwIGz7mO1WrFarRf8tZxR24J6vvNSiLRk5VU27v8siYpqO/1igxkQG4zJBB5mE2azCQ+zCcvJkX2eFhO9o4MYEBtMsK/XaYHl4r8u4+YRcVzRL4oh8aEEensC8Py1/Vh94AQDO4aw6mA2+zKK+CG9wDFEetWBE/z6g828Om1gi71QFpVX0feUOUn+tSbZ8bHJBAkdAhneOYxhnUIZ2imUYF+v8/o6JpOJPtFB9ImuG3DKKm0cya25/VNeZScuzJf4MD9C/M7v60jbYrhBr9sG/eSPGzeOHTt21Nl266230rNnTx588MFGCStQEwySkpLo27dvoxzvQtXe8XX96RJpfluO5HEwq6YFoKLKhpeHmYcv7+XUa0N8Peusxgvw3rojvLfuCH+Z2psZI+IBCPX14l9rkrnv0yRMpjPffl22L5urXl3DqzcOpFtEwAW9p+aUUVDOn77YwfrDuac9d9PwjgzrFMbF3cLPO6A4y8fLQs/IQHpGBjbp15HWzZV9qhsUWAICAujTp0+dbX5+foSFhTm25+bmkpqayrFjxwAcQ5UjIyMdo4BmzJhBdHQ0c+bMAWD27NkMHz6cbt26UVhYyMsvv0xSUhKvvvrqhb27xuJoYXFtGSKuUNsKArA3o4i9GUXcflEnIgLr3ha22w22p+fj7WnBMODLpKN1wsqormH0iwnms81pnCiu5M9f7uLLpGMMjg/hzRWHHfud6+dsf2YxV76ymkemJDBtaEfMZvcdkZKaU8pbqw7x2aZ0Kk/O7BsZ6M3dl3Rh+vA4jaYRaaBGb1tduHAht956q+PzG264AYDHHnuMxx9/HIDU1FTM5h/vRefn53PnnXeSkZFBUFAQAwYMYOXKlQwdOrSxyzsvJpcO5BJxrT7RgXz26xEcLyhj5rwkAI7ml1FcUU21zaCi2saCbUd5d03KWY8xtkc75t5a8/P8h0k9+PuS/by6/BBbjuSx5Ujeafuv+MNYDmUX886qZIorqhnWKZTpw+N55IsdrDpwgke/2MnCpKM8dXVfujdja0tltZ2CsiqqbHY8LCasFgueHiY8zGZOFFeQklPCwqRjrD+cQ8rJUTtQM/nkm9MHM65ne7cOWSLuzGS0ko4ZhYWFBAUFUVBQQGBg4zZ5ZhdVMOSppQCkPDOlUY8t0lJU2+x0feSbevfz9bJQWmkj3N/K4z9LYHLvyNMmkIOakTELth3lSE4pcWG+9IkKoluE/2ktN6ey2w3mrk3huW/3UVZlA2BK3w7MntqbcP/G79NWWlnNF9uO8caKQ5hNkFFYTnmV84s3junejrvHdmGYFvqTFq7bI19TZTNYN+tSOgT5NOqxnb1+t8zea83s1JZbwzDUlCttkofFzMSECJbvz8bTbMLDYq7pcGs2kVVUQYcgb965eTC9o04fzXImEYHeDZ5rxWw2cdtFnZjUJ5Kpr6zmRHElX+04zlc7jnP32C5cPSCaLu38L6gVwzAM1h3O4aP1qSzZnem4nXMqL4uZKru9zu0rD7OJ2FBf2vlbwQTPXNO3zkKQIi2ZOzRtKLA44dRffYbh2k5HIq701ozBri4BgOhgHzY8PJ4P1x/hsYW7AHht+SFeW36I6GAf7h3XlUm9IxvUkbWkopr/bEnng/VHHJ2MoWZytGGdwri0Z3u6R9RMomYymTCMmjloqmx2qqoN/KyWM7YkibQmruwiocDihFNbVNwgZIoINUtm3DwynhuHdeS7PZl8vDGNjck5HM0v48H5O3hw/g76RAdy84h4ruwXdcY1bfJKKlm0K4NVB7JZtjfbcZvJz8vCVQOimTYsjl4dAs7Yqlo7jNvTYgaNDBZpcgosTqjbwmLg2tUURORUnhYzk/t0YHKfDpRV2vhw/RHeW5dCel4ZO48W8of//MCjX+6kf2ww3SMCCPT2pKi8ii2peew+VlhnjbDO4X7cMiqeqwdEE3DK6CgRcT0FFifU6cPiujJEpB4+XhZ+NbozvxrdmeyiCp7+eg8Lth2lvMrO+sO5Z5wLpWdkAON7RTC5TyS9owLVR03ETSmwOEHDmkVannYBVv5+fX9euK4fh7KL2Xokn+ScEsoqaya/6xsdxNBOoecclSQiNdzhj3UFFmfUGSXkujJEpOFMJhNd2wfQtX3LmR1XxF25sgFSXdobyB3WUxAREWlrFFicYFILi4iIiEspsDhBPVhERERcS4HFCXXmYVELi4iItDHusIqPAosT6szDoj4sIiLSRrnyjoMCixM0LYOIiIhrKbA44dR5WNygVUxERKTNUWBxgma6FRERcS0FlgZyh45HIiIizckdrnwKLA3kDidNRETEJTTTrXvTxHEiIiKupcDihDqLHyqwiIiINDsFFidoWLOIiIhrKbA4QRPHiYhIW+YO3SEUWJygqflFRER+0kWimSmwOKFuC4uIiIg0NwWWBtI8LCIiIs1PgcUJmulWRETEtRRYnKA+LCIiIq6lwCIiIiJOceU0HwosTqo9SRrWLCIi0vwUWJzkCJXKKyIiIs1OgcVJtf1YlFdERKQtcZfRsQosTqptYXGT8yYiItLsXLlSjQJLA6kPi4iISPNTYHGSo9Ot8oqIiEizU2BxkivXTxAREWnrFFic5RjWLCIi0na4y50FBRYn/djp1k3OnIiISDMzuXDmuAsKLHPmzMFkMnHfffc5tn3++edMmjSJ8PBwTCYTSUlJTh1r/vz5JCQkYLVaSUhIYMGCBRdSWqNTHxYRERHXOe/AsmnTJt566y0SExPrbC8pKWHUqFE888wzTh9r3bp1XH/99UyfPp3t27czffp0rrvuOjZs2HC+5TU69WERERFxHY/zeVFxcTHTpk3j7bff5sknn6zz3PTp0wFISUlx+ngvvvgiEyZMYNasWQDMmjWLFStW8OKLL/LJJ5+cT4mNTi0sIiIirnNeLSz33HMPU6ZMYfz48Y1SxLp165g4cWKdbZMmTWLt2rVnfU1FRQWFhYV1Hs1B87CIiEhb4i5XvQa3sMybN4+tW7eyadOmRisiIyODiIiIOtsiIiLIyMg462vmzJnD7NmzG62G+uiGkIiItHUtZqbbtLQ0Zs6cyYcffoi3t3ejFvLTnseGYZyzN/KsWbMoKChwPNLS0hq1nrPVp1tCIiIiza9BLSxbtmwhKyuLQYMGObbZbDZWrlzJK6+8QkVFBRaLpcFFREZGntaakpWVdVqry6msVitWq7XBX+t8OYY1N9tXFBERkVoNamEZN24cO3bsICkpyfEYPHgw06ZNIykp6bzCCsCIESNYsmRJnW2LFy9m5MiR53W8JuHodKvIIiIi0twa1MISEBBAnz596mzz8/MjLCzMsT03N5fU1FSOHTsGwL59+4CaVpTIyEgAZsyYQXR0NHPmzAFg5syZjB49mmeffZapU6fy5ZdfsnTpUlavXn1h764RqYVFRETaInf5Q73RZ7pduHAhAwYMYMqUKQDccMMNDBgwgDfeeMOxT2pqKsePH3d8PnLkSObNm8e7775LYmIic+fO5dNPP2XYsGGNXd55Ux8WERFp61w40S0mw12i0wUqLCwkKCiIgoICAgMDG/34/f+ymPzSKpY+MJqu7QMa/fgiIiLuqNpmp+sj3wCQ9OcJBPt6Nerxnb1+ay0hERERcXsKLE76cfFDl5YhIiLSJimwOMnRh8XFdYiIiDQnd7nuKbA4SS0sIiLS1rlyIWAFFic5Fj90m6wpIiLSdiiwOE3DmkVERFxFgcVJjhYWBRYREZFmp8DiJK3WLCIibZG7/KGuwNJA6sMiIiJtlgv/eldgaSBX9pAWERFpqxRYRERExO0psIiIiIjbU2BxknquiIhIW+QufTcVWBrIlUtri4iIuJIrr4EKLCIiIuL2FFhERETE7SmwiIiIiNtTYHGSu8z0JyIi0pzc5fqnwNJA6nQrIiJtlSsvgQosIiIi4vYUWERERMTtKbA4zU1u4omIiLRBCiwNpMUPRUREmp8Ci4iIiDjF5MKRJwosIiIi4vYUWERERMTtebi6gJbCXSbOERERaS5F5VX8d/txV5cBKLA0mCaOExGR1qKwvIptqfmk5ZYyKC6EXh0CAVh76ATvrklhye5Mx75eFjOeFtddBBVYRERE2pjv9mTyzDd7OZhd7LiDYDLB1QOiqaiy89WOuq0qNw7ryBV9O2D1sLig2hoKLCIiIm2EYRi89N0BXlx6wLGtY6gvYf5ebEvN5/OtR4Ga8HLTsDhuGBpLQodAl44OqqXAIiIi0gYUV1Tzl//u4rPN6QDcMjKeu8d2oX2gNwBbjuTy2aZ0LBYTNwyJJTEm2IXVnk6BxUnqcysiIi3VieIKfv76Wo7klGIywaNTErjtok519hkUF8qguFAXVVg/BZYGcn2jmIiISMN8mXSMIzmlAHzyq+EM7xzm4ooaTvOwiIiItHKnju5Zvi8bu73l3TdQYBEREWnlrh8Si7+15qbKGysO8eRXe1xcUcMpsDjJ0MxxIiLSQlk9LOx4fCLPXNMXgH+tSeb5b/e1qGvbBQWWOXPmYDKZuO+++xzbDMPg8ccfJyoqCh8fH8aOHcuuXbvOeZy5c+diMplOe5SXl19IeY2q9pS6w9AuERGRhjKZTNwwtCOzLusJwCvLDvLg/B9cXJXzzjuwbNq0ibfeeovExMQ62//617/ywgsv8Morr7Bp0yYiIyOZMGECRUVF5zxeYGAgx48fr/Pw9vY+3/IaXe39PrPyioiItGB3ju7sCC2fbU4nq9B9GgfO5bwCS3FxMdOmTePtt98mJCTEsd0wDF588UUeeeQRrrnmGvr06cN7771HaWkpH3/88TmPaTKZiIyMrPNwJ7X9k8xqYRERkRbMZDLx6zFdGNAxGIAlezLP/QI3cV6B5Z577mHKlCmMHz++zvbk5GQyMjKYOHGiY5vVamXMmDGsXbv2nMcsLi4mLi6OmJgYrrjiCrZt23bO/SsqKigsLKzzaEp2o7aFRYFFRERavlFdwgF4ZMFOqm12F1dTvwYHlnnz5rF161bmzJlz2nMZGRkARERE1NkeERHheO5Mevbsydy5c1m4cCGffPIJ3t7ejBo1igMHDpz1NXPmzCEoKMjxiI2NbehbaRBHYFE3ZRERaQWuGhDl+Hj2f3e7sBLnNOjym5aWxsyZM/nwww/P2b/kpx1TDcM4Z2fV4cOHc9NNN9GvXz8uvvhiPvvsM7p3784//vGPs75m1qxZFBQUOB5paWkNeSsNZncsDqUWFhERafm6tg/glpHxAHyw/gh/XbTXtQXVo0GBZcuWLWRlZTFo0CA8PDzw8PBgxYoVvPzyy3h4eDhaVn7ampKVlXVaq8s5izKbGTJkyDlbWKxWK4GBgXUeTal26JdFgUVERFqJx65M4A+TegDw+opDHMsvc3FFZ9egwDJu3Dh27NhBUlKS4zF48GCmTZtGUlISnTt3JjIykiVLljheU1lZyYoVKxg5cqTTX8cwDJKSkujQoUNDymtSP3a6dW0dIiIijcVkMnHLyHi8LGYMA4rKq11d0lk1aC2hgIAA+vTpU2ebn58fYWFhju333XcfTz/9NN26daNbt248/fTT+Pr6cuONNzpeM2PGDKKjox39YGbPns3w4cPp1q0bhYWFvPzyyyQlJfHqq69e6PtrNLV9WHRLSEREWpPnvt1Hpc1ObKgP3dr7u7qcs2r0xQ//+Mc/UlZWxt13301eXh7Dhg1j8eLFBAQEOPZJTU3FfErv1fz8fO68804yMjIICgpiwIABrFy5kqFDhzZ2eefFMAwMtbCIiEgrs+FwDnPXpgDw1FV9MbvxRc5ktKR5ec+hsLCQoKAgCgoKGr0/i91u0PnhrwHY9ugEQvy8GvX4IiIirnDvJ9tYuP0YvxgUw/PX9nNJDc5evzVI1wn2UzKd5mEREZHWoqSips/KkPiQevZ0PQUWJ5y6CrdJ/2MiItIKGIbBd3uzAKiyuf/NFl1+naAWFhERaU1W7s9m3N9WOD5vCde2Ru902xqdGlg0D4uIiLR0v/tkGwVlVQB0Cvfj6gHRLq6ofmphcUKdW0LKKyIi0sLdNqqT4+OUnBIW7Truwmqco8DiBN0SEhGR1uTecV156Yb+DOwYjGHAmysOu7qkeimwOMFuPzWwuLAQERGRRmAymZjaP5pxvWqWzekQdPb1Ad2FAosTbKcEFosSi4iItBLb0/IB6B+rYc2tgu3kLSGzSVPzi4hI69EvNhiAhduPUmWzu7aYeiiwOKG2hUWtKyIi0prcNDyOUD8vDmWX8OmmNFeXc04KLE5QYBERkdYoyMeTSb0jAdiUkuvias5NgcUJjsCi20EiItKKJJ8o4X8/HANgVNdwF1dzbgosTlALi4iItEaPLNhBUXk1AzsGc1V/9548ToHFCQosIiLS2uSVVLL2UA4AL14/AC8P944E7l2dm6gdJWQx679LRERah8MnigEI8PagY5ivi6upn9YSckK1rTawuLgQERGRC5RdVMG/1iTzwbojACTGBLm4IucosDihdmp+D7WwiIhIC7ZgWzqzPt9BeVXNnCt9o4OYc3Wii6tyjgKLE6pP9mFRXhERkZYq+UQJf/zPD1TZDPrFBPHbS7sxrmd7zC2kf6YCixNq1xJSC4uIiLRUT321myqbweju7Zh7y5AWE1Rq6QrsBEcLS8s6tyIiIgCUV9n4bm8WAI9c3qvFhRVQYHGKWlhERKQlS0rLxzAg3N9K9wh/V5dzXnQFdsKPfVhaXiIVEZG2rbLazqvLDgJwUdewFruIr/qwOKF2BUt3n1RHRETkVHklldz14RY2JOdi9TDz6zFdXF3SeVNgcUJFdU1gsSqwiIhIC1FZbee29zaxLTWfAKsHr0wbSK8Oga4u67wpsDihvMoGKLCIiEjL8eRXu2vCircH/7lrJD0iA1xd0gXRFdgJP7awWFxciYiISP0+25TG+ydnsn35hgEtPqyAAotTKmpbWDz13yUiIu7vzwt3ApDQIZBLerZ3cTWNQ1dgJ6gPi4iItCTBPl4AzLq8p4sraTy6AjtBt4RERKQlMaiZjqM2uLQGCixOUKdbERFpSToE+QBwIKvIxZU0Ho0ScoKjhUV9WERExI0ZhsGagznsz6wJKh6W1nPdUmBxQmVtYGlFJ15ERFqX0spq/u/f2/l6RwYAHYK8GdujnYurajwKLE6onenWU4FFRETc1L2fJLF0TyaeFhPThsVx77huBHp7urqsRqPA4oQq28nFDxVYRETETS3dkwnAX6b24ZdDO7q4msanK7ATfmxhaZkLRomISOuXcHLa/eP5ZS6upGkosDih2q5bQiIi4t5+PaYzAAuSjrq4kqZxQVfgOXPmYDKZuO+++xzbDMPg8ccfJyoqCh8fH8aOHcuuXbvqPdb8+fNJSEjAarWSkJDAggULLqS0RlVZXXNLSIFFRETcVYhvzZwrZZU2F1fSNM77Crxp0ybeeustEhMT62z/61//ygsvvMArr7zCpk2biIyMZMKECRQVnX0s+Lp167j++uuZPn0627dvZ/r06Vx33XVs2LDhfMtrVLUtLB66JSQiIm7q3TXJAFzcrfWMDDrVeQWW4uJipk2bxttvv01ISIhju2EYvPjiizzyyCNcc8019OnTh/fee4/S0lI+/vjjsx7vxRdfZMKECcyaNYuePXsya9Ysxo0bx4svvng+5TW62j4sXmphERERN1RcUc3G5FwAbhoe5+JqmsZ5XYHvuecepkyZwvjx4+tsT05OJiMjg4kTJzq2Wa1WxowZw9q1a896vHXr1tV5DcCkSZPO+ZqKigoKCwvrPJrKj6OE1MIiIiLu50BmESWVNsL9vRgQG+zqcppEg4c1z5s3j61bt7Jp06bTnsvIqJmsJiIios72iIgIjhw5ctZjZmRknPE1tcc7kzlz5jB79uyGlH7eNA+LiIi4Mw9zzfWpymZgaqV/WzfoCpyWlsbMmTP58MMP8fb2Put+pp/8bxmGcdq2C33NrFmzKCgocDzS0tKceAfnp9pW2+m2lX4XiIhIi9YxzBeAgrIqx3IyrU2DWli2bNlCVlYWgwYNcmyz2WysXLmSV155hX379gE1LSYdOnRw7JOVlXVaC8qpIiMjT2tNqe81VqsVq9XakPLPm1pYRETEna09eAIAD7Op1S7U26B3NW7cOHbs2EFSUpLjMXjwYKZNm0ZSUhKdO3cmMjKSJUuWOF5TWVnJihUrGDly5FmPO2LEiDqvAVi8ePE5X9OcagNLbZObiIiIO6iy2Xlo/g/85qOtAMSF+dZ7R6OlalALS0BAAH369Kmzzc/Pj7CwMMf2++67j6effppu3brRrVs3nn76aXx9fbnxxhsdr5kxYwbR0dHMmTMHgJkzZzJ69GieffZZpk6dypdffsnSpUtZvXr1hb6/RlHb6dbLo3V+E4iISMs0b2Mq8zbVdIn4+cAYHrqsp4srajqNvpbQH//4R8rKyrj77rvJy8tj2LBhLF68mICAAMc+qampmE9prRg5ciTz5s3jT3/6E48++ihdunTh008/ZdiwYY1d3nmpVguLiIi4oYNZxQDcMCSWZ36eWM/eLZvJMAzD1UU0hsLCQoKCgigoKCAwMLBRjz34yaWcKK7g63svJiGqcY8tIiLSECUV1RiAv9WDz7em88Bn2+kTHcj/fnexq0s7L85ev7VasxN+XEtIt4RERMQ1DMPg9//ezudba9YKignxYXLvSAB2Hi1kX0YRPSIDznWIFk33OJzw47Bm/XeJiIhrbD6S5wgrAOl5ZbyzOtnxeWpuqSvKaja6Ajuh0qa1hERExLVqFzX09bLw/e/H8NdfJJIYE4TZBJf0aMfo7uEurrBp6ZaQE6o1D4uIiLjYiC5hdGnnx6HsEu7+aCuf3TWC6wbHOjU5a2ugK3A9DMPAfrJbsrkNfEOIiIh78rSY+dctQwj3t7I3o4j31qQAp88U31opsNTj1DFUFnPb+KYQERH3FOTjSUyIDwAbU3JdXE3z0i2hethPSSzKKyIi4gppuaW89N0B/rMl3bFtbI/2Lqyo+Smw1MN+SgtLW2l2ExER91Fts3P5y6soKq8GwNvTzC+HduS2UfGuLayZKbDUQy0sIiLiSrkllY6w8vGvhjGic1ib/ANafVjqcWofFnW6FRGR5hbq5+X42ISpTYYVUGCpV90Wlrb5TSIiIq7jYTEzsGMwUNPa0lYpsNTj1MCivCIiIs3tRHEFu44VAhB9coRQW6Q+LPWw65aQiIi4QLXNzmeb03l20V4qqu0E+XjSI6L1rhVUHwWWepy6mLXmYRERkeawcn82f/nfbg5mFQPQq0Mgf/15Ij5eFhdX5joKLPWo28LiujpERKRtWHcohxn/2ghAsK8n917ajRkj4vBo48vDKLDUw2Y/tQ+LEouIiDStf29Jc3y84g+XEOTj6cJq3EfbjmtOqL0lpNYVERFpDkPiQ4Gabgj/2ZJOZbXdxRW5BwWWemjhQxERaU7XD47l6gHR2OwGT/xvNxf/9Xvsp/ZPaKMUWOphd7SwKLCIiEjTM5tN/O3aflwzIBqAzMIK5nyzp84gkLZIgaUetYFFeUVERJqL2Wzihev78/y1/QB4e1Uyi3dnurgq11JgqYehW0IiIuICGQXlVFTb8LTUXH92Hi1wcUWupVFC9ahtYdEcLCIi0lw+2ZjKY1/uotJW0+HWbIJRXcNdXJVrKbDUo7afkxpYRESkORzJKWHW5zsA6BcTxNge7ZmQEEGf6CAXV+ZaCiz1UKdbERFpTik5pQBEBXnzxT2jNAfYSerDUg/NwyIiIq4Q5OulsHIKBZZ6aB4WERFpTiUV1QCOzrZSQ4GlHj8Oa9Y3joiINL3v92YB0D822LWFuBkFlnrUriWkW0IiItLUCkqrWLQzA4DL+nRwcTXuRZ1u62E/uYSDhjWLiEhjKa+y8Z8t6aw+cILckkpshkE7fytJafkUV1TTMzKAYZ1CXV2mW1FgqYdNo4RERKQRrTuUw0Of/8CRk6OBfsrPy8JfpvbBrD+U61BgqUftLSG1sIiIyIXILqpgztd7+HzbUQAiA72ZMTKOuFA/ALKKyokM9GZkl3CCfD1dWapbUmCph2a6FRGRC2G3G3y04Qh//XYfReXVmExw49COPHhZTwK9FUycpcBSD3W6FRGR85VVVM4Dn25n9cETAPSNDuLJq/rQTyOAGkyBpR523RISEZEGKq+y8d7aFF5bfoiCsiq8Pc08NLkn00fE63pynhRY6qFOtyIi0hAbk3O595NtZBSWA5DQIZCXfzmAru39XVxZy6bAUg91uhUREWdU2ezMXZPCX7/dS5XNIDrYh/sndOfqAdG6hjSCBk0c9/rrr5OYmEhgYCCBgYGMGDGCb775xvF8ZmYmt9xyC1FRUfj6+jJ58mQOHDhwzmPOnTsXk8l02qO8vPz83lEjU6dbERGpz65jBVz5j9U89fUeqmwGl/Rox9IHxvCLQTG6fjSSBrWwxMTE8Mwzz9C1a1cA3nvvPaZOncq2bdtISEjgqquuwtPTky+//JLAwEBeeOEFxo8fz+7du/Hz8zvrcQMDA9m3b1+dbd7e3ufxdhqf7eTEcbolJCIiZ/KfLek8+sVOyqpshPh68tBlPbl2UKzmUWlkDQosV155ZZ3Pn3rqKV5//XXWr1+Pp6cn69evZ+fOnfTu3RuA1157jfbt2/PJJ59wxx13nPW4JpOJyMjI8yi/6emWkIiInElZpY0/fbGT+VvTAbi4Wzgv3zCAED8vF1fWOp33WkI2m4158+ZRUlLCiBEjqKioAOq2jFgsFry8vFi9evU5j1VcXExcXBwxMTFcccUVbNu2rd6vX1FRQWFhYZ1HU3DcElILi4iInJSWW8rPX1/L/K3pmE3w+wndmXvrUIWVJtTgwLJjxw78/f2xWq3cddddLFiwgISEBHr27ElcXByzZs0iLy+PyspKnnnmGTIyMjh+/PhZj9ezZ0/mzp3LwoUL+eSTT/D29mbUqFH19n2ZM2cOQUFBjkdsbGxD34pTaltYlFdERARgU0ouP3tlNbuPFxLm58VHdwznd+O6qSW+iZkM42QTgpMqKytJTU0lPz+f+fPn884777BixQoSEhLYsmULt99+O9u3b8disTB+/HjM5ppM9PXXXzt1fLvdzsCBAxk9ejQvv/zyWferqKhwtOoAFBYWEhsbS0FBAYGBgQ15S+f0ZdJRZs5LYmSXMD7+1fBGO66IiLQ8abmlXPGP1RSUVZEYE8QbNw0iKtjH1WW1aIWFhQQFBdV7/W7wsGYvLy9Hp9vBgwezadMmXnrpJd58800GDRpEUlISBQUFVFZW0q5dO4YNG8bgwYOdPr7ZbGbIkCH1trBYrVasVmtDy28w9WERERGAkopqfv3BFgrKqugXE8Snvx6Bt6fF1WW1Gefdh6WWYRh1WjoAgoKCaNeuHQcOHGDz5s1MnTq1QcdLSkqiQ4cOF1pao/hxan4FFhGRtspmN5g5bxu7jxcS7u/FazcNUlhpZg1qYXn44Ye57LLLiI2NpaioiHnz5rF8+XIWLVoEwL///W/atWtHx44d2bFjBzNnzuSqq65i4sSJjmPMmDGD6Oho5syZA8Ds2bMZPnw43bp1o7CwkJdffpmkpCReffXVRnyb50/zsIiItE0V1TbS88pIzS3lP1vSWbonCy8PM2/NGEy0bgM1uwYFlszMTKZPn87x48cJCgoiMTGRRYsWMWHCBACOHz/OAw88QGZmJh06dGDGjBk8+uijdY6Rmprq6NcCkJ+fz5133klGRgZBQUEMGDCAlStXMnTo0EZ4exdO87CIiLQdldV23l+XwscbU0k+UcKpvTxNJvjbtf0Y2DHEdQW2YQ3udOuunO2001AfrEvh0S93cVmfSF6/aVCjHVdERNxLeZWNOz/Ywsr92Y5tvl4WOob60j0igBkj4hgcH+rCClunJut029ZU2mrynJfHBXf3ERGRBiquqOZ4fhmBPp6E+1ub7PZ8WaWNO97fxJqDOfh4WnhkSi8m94kkzM8Lk1rY3YICSz0qq2vuCXlaFFhERJrLieIK3l55mPfXHaGsygZAmJ8Xt13UiZtHxuNvbbzLV2llNbe+u4kNybn4eVl499ahDO2klhR3o8BSj6qTnVjUwiIi0jzWHjrB3R9tJb+0CgB/qwdlVTZySip57tt9fLwhldemDaRfbPB5Hb+wvIof0gqottvpGOrLd3uy2JCci4+nhfdvH8qgOIUVd6TAUo/aFhYvtbCIiDS5D9YfYfbCXVTbDXpGBvDg5J6M7dGOarvBf7cf44Ul+0nPK2P6Pzew4g+XOD0VfvKJEl5cup+V+7PJOxmEfurWUfEKK25MgaUetS0snhbdwxQRaSoHs4p4Ycl+vt6RAcBV/aN45ueJjrlOPC0mrhkYw8TekfzsldUczi5h4fZj3Dwy/qzHLKu0MX9rOl8mHWVTSl6d56KDfQjw9uDwiRIqq+30jgrklnMcS1xPgaUelbolJCLSJAzDYPn+bP6zJZ1vdhzHbtQMHf7DpB78ZkyXM3Z29bd6MH14HLP/u5v5W9PPGFgqqm18timNl78/SHZRzcSmJhNc0qM9d4/tQvfIAAK9PYGaVvS80kraB1jVudbNKbDUo7aFxcOswCIi0lhOFFdwz0db2ZCc69g2ISGC+8d3JyHq3FNT/KxfFE99tYcf0gvYmJzr6CBbXmVj4fZj/H3Jfo4XlAMQE+LDjBFxXNkvig5Bp0/25uVhJiLQuxHfmTQVBZZ61E7Nr1tCIiKNIzWnlBn/2kBKTilmE1w7KJbpI+LoEx3k1OvD/K38rH8Un289ynVvriMqyBuLxURmYYWj32FEoJV7LunKDUM6qoW8lVBgqUe1rXZqfn3Di4g0hgfn/0BKTikxIT788+Yh9IgMaPAxZv+sN9lFFaw6cIJjJ1tTADoEeXPLyHhuHhmvtX5aGQWWelSfbGHx0FpCIiIXzDAMtqTWdIB9bdrA8worAAHenrx/21DScsvIL6ukymYQ7u9Fx1Bf9UVppRRY6lEbWMwKLCIiF6ywvNpx26Zre/8LOpbJZKJjmC8d8W2M0sTN6T5HPWx2DWsWEWkse44XAjXDin299DezOE+BpR4/9mFRYBERuVA7jxYA0LuekUAiP6V4Ww+b+rCIiJxVaWU13+zI4If0fDILKzCbwdvDQrsAK+0CrGQWluNhMTMkPoTR3dqRllsKXPjtIGl7FFjqUduHRaOERETq2nm0gLs/2krqyRByLq8DPp6WHxcy9Lc2cXXS2iiw1MPmCCwuLkRExI0UlFZx29xNZBVV0CHImyv7RREd7IPJVDMl/vGCcjIKyukQ7E15lY1vd2WSW1IJQJCPJxN6Rbj4HUhLo8BSD4OTo4Q0TE5ExGFDcg5ZRRUE+3qy6L7RBPl4nnP/v0y1cySnlPS8UhJjggl1ctFCkVoKLCIi0mC1I3x8PC0EWOu/lHhazHRt76++K3LedKNDREQaJK+kkpLKagCOF5SzLS2vnleIXDi1sIiIiFN2Hi3g/k+TOJBV7Njm42nR4oHSLBRYRESkXpXVdu75eCtHcmpGBMWH+TI4PpTbL+pETIhmmpWmp8BSDxPqbCsibVtxRTXvrDrMkZxSfDwtrPjjWNoHqFVFmpcCi4iInNWh7GKue2MdOSeHJE8b1lFhRVxCgUVERM5qYdIxR1j5v4nduWtMFxdXJG2VRgmJiMhZdTllGPLtF3XGQ7NoiouohaUedsM463NbU/NYsS8bD7OJQfEhDI4LxctDP8wi0npM7h3p+Hjh9qNcP6SjC6uRtkyBpR61gcV0yky3ldV2Hl6wg/9sSa+zb4cgbx67sjeT+0QiItIaeHmYGdAxmG2p+Tw4f4cCi7iMmgPqUdvAUhtXqmx2fvfJVv6zJR2zCaYkduBn/aII8/PieEE5d324hU83pTZpTeVVNrIKy6m22Zv064iIAEzp28HVJYiohaU+tTeE7IbBzqMFvLh0P0v3ZOHlYebN6YO4pEd7oCZEPPnVbj5cn8qD83cANPpfIoXlVTzzzV7mb0mnotqOr5eFKxOjePjyXgT5nnsdDxGR89U/NhiA6GAf1xYibZoCS31OJpaZ85Icm7w8zLw1fRBjT4YVAG9PC09M7YOH2czctSk89PkOrB4WrhoQ3eAv+eMK0T/ehsorqeSmf25g17FCx7bSShufbk5jb2YRH98xDD8n1vMQEWmowJMLGxZXVLu4EmnLdIWrR4D3j/9FAVYPBseHcM8lXRkcH3raviaTiceuTKDKZuejDanc92kSf120l4ggbwbEhvC7S7sSco4VSksrq3nif3v4fGs6hgGX9mzPxd3DySup5LPN6aTmlhLu78XLNwxgWOcwNhzO4Z6Pt7I9LZ9Xlx3kj5N7Nsn/gYi0bUmp+UBNPz0RV1FgqccjU3oxrHMovaOCGN45rE6rx5mYTCb+MrUPXh5mPlh3hGMF5RwrKGdbaj6fb0tn1mU9uW5wbJ1OvGWVNral5vHYwl111uhYtCuDRbsyHJ+3D7Dy8a+G0bV9AAAju4Zz99iuPPX1HnYf/7HlRUSkoex2g293ZXAou5hO4f6M6dGOskobH204wuvLDwEwvleEi6uUtkyBpR6d2/lzZ7uGLYduMZt47Mre3DeuOwezi0nPK+X15YfYm1HEg/N38M/VySTGBONhNrHjaAF7M4oct4HaBVh57heJtA/wZv7WdA5kFRPs48mQ+BCuHhiD/09u+0Sc/Isnv7Sqcd6wiLRJ/1qTzJNf7Tnr85f2bM/dl2jSOHEdBZYmFOTryaC4EAbFhTClbwfeXZPC35bsY39mMfszi+vs2z7AykVdw5l1eS/aBVgBSIhKqPdrRAfXBJaswvLGfwMi0mbULmoIEBfm6/i8X2wwd17cmcv6RGKup4VZpCkpsDQTD4uZX43uzHVDYlm+L4v0vDLKq2z0jAxkQMdgOgR517lN5KzuETW3h44VlHPj2+t55+bB+HrptIpIw3Q4+cdP9wh//ve7iymrsuFhNqkzv7gNk2GcYyrXFqSwsJCgoCAKCgoIDAx0dTnNKv6hrxwfm03gZ/Ug1M+LEF8vwvy86NUhkIu6hTOgYzBWD4sLKxURd5VbUsn4F1aQW1LJpN4RPHV1X8L9ra4uS9oAZ6/fDZo47vXXXycxMZHAwEACAwMZMWIE33zzjeP5zMxMbrnlFqKiovD19WXy5MkcOHCg3uPOnz+fhIQErFYrCQkJLFiwoCFltXnbH5tIt5PrfdgNKCqv5khOKUlp+Xy3N4tXlh3khrfWM/AvS/jTFzs4ll/m4opF5HwYhsGe44V8mXSUjzYc4asfjlNZ3TgTSIb6eTHnmr5YzCa+3ZXJ+BdWMH9LOq3kb1ppBRrUwvLf//4Xi8VC165dAXjvvfd47rnn2LZtGwkJCYwcORJPT0/+9re/ERgYyAsvvMCiRYvYvXs3fn5+ZzzmunXruPjii3niiSe4+uqrWbBgAX/+859ZvXo1w4YNc/qNtOUWllqGYZBdXEFhWTV5pZXkllSSVVTB5pRc1hw8wYnimhVXA6wePHl1H6b2b/gcMSLiGuVVNn79wRZW7M+usz0mxId3bh5Mz8jG+b33Q3rNFPx7To48vCKxA//45YDzumUt4gxnr98XfEsoNDSU5557josvvpgePXqwc+dOevfuDYDNZqN9+/Y8++yz3HHHHWd8/fXXX09hYWGdlprJkycTEhLCJ5984nQdCiznZrcbrE/O4blv97Ht5JwKv5/Qnd+N6+bawkTEKW+vPMxTX++pWWw1LoRAH0+S0vLJLqqgfYCVb+8bfc55nhqiymbn7VWHeXHJASptdl68vv95TYIp4owmuSV0KpvNxrx58ygpKWHEiBFUVFQA4O3948RCFosFLy8vVq9efdbjrFu3jokTJ9bZNmnSJNauXXvOr19RUUFhYWGdh5yd2WxiZJdw/v3rEfz2kpoWsr8t2c+agydcXJmI1KfKZue9dSkAzJ7am09/PYK3Zwxm6f1j6BzuR1ZRBe+sPszBrCI2HM5hf2bRBd3K8bSYuXtsVy7qFg5wWquOiCs0OLDs2LEDf39/rFYrd911FwsWLCAhIYGePXsSFxfHrFmzyMvLo7KykmeeeYaMjAyOHz9+1uNlZGQQEVF3MqKIiAgyMjLO8ooac+bMISgoyPGIjY1t6FtpkzwsZv5vUg9uGl6zztHL39Xfx0hEXMNuN1i2N4tr31hHel4ZIb6e/HxgjOP5IF9PbruoEwCvLjvE+BdWcv1b65n495VM/PtKthzJPe+vvWR3Jt/vzQKgb3TQhb0RkUbQ4MDSo0cPkpKSWL9+Pb/5zW+4+eab2b17N56ensyfP5/9+/cTGhqKr68vy5cv57LLLsNiOffIlJ/eGzUMo977pbNmzaKgoMDxSEtLa+hbadNuG1XzS25bWj4V1TYXVyMip7LZDd5bm8LY55dz69xNJKXl4+VhZs41iXh71v19OrV/FH2ia5rRA7w96BTuh9XDzIGsYm59dxMHs4rP9CXq9fnWdMfxbx4Zf0HvR6QxNHiAvZeXl6PT7eDBg9m0aRMvvfQSb775JoMGDSIpKYmCggIqKytp164dw4YNY/DgwWc9XmRk5GmtKVlZWae1uvyU1WrFatWQu/PVKdyPAG8PisqrOZBZTB/9BSXiNv74nx+YfzIw+Fs9uHFYR26/qBMRgaev5RPg7cl/f3sRFdV2R5gpLK/iln9tZGtqPrfO3ch/f3sRwb7O92+pqLaxMbmmdeaq/tH1Lkki0hzOuw9LLcMwHP1XagUFBdGuXTsOHDjA5s2bmTp16llfP2LECJYsWVJn2+LFixk5cuSFlibnYDKZHEvGf7ThiGuLERGHzSm5LNhWE1b+NKUXGx8Zx8OX9zpjWKllMpnqtLwEenvy9ozBdAz1JS23jKe/PvuU+2dyILOYnJJKPMwmRnUNP783ItLIGhRYHn74YVatWkVKSgo7duzgkUceYfny5UybNg2Af//73yxfvpzDhw/z5ZdfMmHCBK666qo6nWpnzJjBrFmzHJ/PnDmTxYsX8+yzz7J3716effZZli5dyn333dc471DO6t6TI4Q+2ZjGvzfrlpqIq2UVlnPXh1uwG3BlvyjuuLjzec9cHeZv5YXr+gHw2eZ0Fu86c7/AapudD9cf4YHPknj66z3szywiItAbD7OJarvBo1/spMrWOHO9iFyIBgWWzMxMpk+fTo8ePRg3bhwbNmxg0aJFTJgwAYDjx48zffp0evbsyb333sv06dNPG5qcmppapxPuyJEjmTdvHu+++y6JiYnMnTuXTz/9tEFzsMj5GRIfyu0nO+z94T8/8Py3+zRJlIgLPb94HyeKK+kZGcCzP+97wccbfMrP+P/9ezvHC+pOGllRbePWuZv40xc7+XzrUd5aeZgrXl7Nkt2ZPHFVH8wm+HRzGje9s4H80soLrkfkQmhq/jbObjd4ZtFe3lp5GIBfj+7Mg5N7apEzkWZ2vKCMCS+spLiimo/uGNZot2Iqq+0MemIJRRXV/GFSD+45Oa0BwKzPd/DJxlS8PMzcPCKOvRlFrDpQM9VBv9hgvCwmNqXkAfDoFQmO8CPSmJy9fmtVqzbObDbx8OW9iA3x4dEvd/HmysN4eZj5/cQeri5NpM0oqajmtrmbKa6opnuEP8M6hTbKcW12gyf+t5uiimoAwv3rdrxduicTgL9d248r+0VhGAZvrjzMS0sPsD0t37Gf2QTDOzdOTSLnS4FFAJg+Ih6z2cQjC3byj+8PktAhkMv6dnB1WSKtXvKJEmb8awNpuWWE+3vxr1uG4GG54PEQVNvsdH3kxxnE7720K9cO+nG+qspqOwVlVQD06lCz6rvJZOKuMV24ZmA0/91+nO1p+ZhNcN3gWHpHaSShuJYCizhMGxbHoawS/rUmmZnzkgjztzK0kf7SE5HTlVRUc8nzyx2fvzl9EDEhvo1y7A/X/zj671cXd+KBU1pNbXaDZ77ZS2W1nXB/62lfs32At27/iNu58BgvrcrDl/dkfK/2VNrsvPTdfleXI9Kq1d528TCb+OD2oQyKa7w/EI4XlDs+jgvzo8pmp7SymvfXpTDub8v515pkAB69otdpk9GJuCO1sEgdHhYzvxnbhaV7skjOLnF1OSKtWkV1zXBhH09Lo7dm/npMF3YeK2DNwRz+9MVOnvt2H9U2OyWVNTNbB/t68qcpCVq1XVoMBRY5Te24MS8PNcCJNKVRXcMJ9/fiRHElW4/kM6JLWKMdO9TPiw9uG8a/1iTzxorDnCiumeAzLsyX2y/qxC8GxZz3HC8irqDvVjlN5cm/+jwboeOfiJydl4f5ZGiopNre+JOzmc0m7ri4M7eMjGfnsUJsdjv9Y0M01b60SAoscpraOVjsrWOKHhG3lZZbSmpuKSYTJMYEN9nX8bCYHUtxiLRU+hNaTuNvrcmxeaVVmvlWpImknCjhlnc3AjA4LoQgH08XVyTi3tTCIqfp2t4fL4uZ3JJK1h3KYaQWPxNpFAezitiUksd3e7JYuT+bSpudDkHezLkm0dWlibg9BRY5jbenheuHxPLB+iPc8/FWnryqL5N6RzTKZFYibVFJRTUvf3+At1Ye5tRGy1Fdw/jbtf2JDDr7SswiUkOBRc7oD5N7sC0tj51HC7nn4614mE3EhPgQF+ZH9wh/ekYG0qtDIF3a+2H10BwOImez/nAOM+dtI7OwZpRO/9hgRndvx+V9I+kREYDJpA6wIs7Q4odyVmWVNt5YcYh/rUmmqLz6jPt4mE30jg5iQq/2TEiIpHuEv1O/gA3DIPlECasPnuCH9ALKqmxYLWbiw/2Y3CeS7hEBjf12RJpdfmklo/+6jMLyauLCfHl0SgLjEyJcXZaIW3H2+q3AIvWy2w0yi8pJOVFK8okS9mUUsiejiL3HCyk8Q5CZNqwjwzqHER3sTYC3J14WMx4WE6k5pWxLy2djci7b0/PJL60669ccEh/C3Zd05ZIe7ZvyrYk0qYXbj3HvJ9uIDfVhyf1jNKOsyBkosEiTMwyDo/llrDpwgr8t3u+YmMpZXhYzAzoGM6JLGEE+npRW2tiWmu9YQRYgxNeT+HA/4sNqHl3b+3Nx93ACvTWiQtzfsr1Z3Dp3E35eFh6ZksB1g2PUF0zkJxRYpNnllVSyMSWX9Ydz2JqaT15JJUXlVVTZDCqqbUQGeZMYE8yQuBAGxoXQMzLwjLPpbjmSxy/fWk+l7cwTaXlaTFyRGMWjVyQQ6ufV1G9LzqK0spptqflsS63p61RaZaNXhwD+MLGHLsonlVfZ+Pnra9l1rBCAoZ1Cef+2oWppETmFAou0eCUV1RzJKSUlp4SUnBKOnChlS2oeB7OKAejW3p///u4i/fJvRlU2O4t3ZfLp5jTWHjxBtf30Xx8JHQL57K4Rjvl82rrSymo+3pDK35fsp6TSxsOX9+TO0V1cXZaI23D2+q3fKOK2/KweJEQFkhBV9xs4KS2fO97bzIGsYv69JZ3pw+NcVGHrlFtSyfGCMux2iAnxIdjXk+ziCuZtTOOjDUcco10AooK8GRwfSmJMEPsyivhmZwa7jxfy4foj3DVGF2UAXy8P7ri4M+l5Zcxdm8LTX+/ltlGd1Aol0kAKLNLi9I8N5tZR8Tz37T6W7s5UYGkk2UUV3PPxVjYm59bZ7utloazK5pg/JNzfyi+HxnL1gGg6hfvVGRXWpb0/z3yzl22pec1ZeotwamP25iN5DO/ceAsdirQFivjSIk04OTR0xf5s7vloKyv3Z5PTwE6/8iOb3eDODzY7wkqwryftAqwAlFbWhJWBHYN56Yb+rH3oUn4/sQed250+hL125Jf6Fp3urrE/tjh9tCHVhZWItExqYZEWqXtEAPde2pWXvz/IVzuO89WO4wDEhvpwed8O/PaSrgQ00UiitNxSNh/J5UBmMSk5Jdjt0D7QysguYVzaM+KMHYndmWEY/PXbvWxLzcff6sGCu0fS7eQ8OOVVNjIKyrGYTcSG+tZ7rKS0mpaVfk24kF9L1SHIh8m9I1m0K4PCsrMP6ReRM1NgkRbrgYk9uKxvB/65OpmtR/JIzikhLbeMN1ccZltqPp/eOfyCZxG12w32ZRax+UgeW1Jy2ZSSx9H8sjPu+/66I4T7ezFzXDduHBaHxezeM5gahsGagzn8a00y3+/NAuAvU3s7wgrULNMQH+7n1PGqbHa2pxUAMKBjSOMX3AqE+de0PGmhQ5GGU2CRFq1Xh0Cev7YfAMUV1Szfl8XvP9vOxuRcfkgvoF9scIOPmVNcwb/WJPPZ5nSyi06/zeRhNtEnOog+0YF0CvfHy2Ii+UQpX+04RmZhBY9+uYvFuzN56qq+xIb6uNXU64XlVWxKzmVjci5L92RyKLsEAJMJHr+yN9cMjDnvY5dW2CirsgHg7dmyWpmaQ3mVjS+2HQXg+iGxLq5GpOVRYJFWw9/qwRWJUfxv+3EW7cpg2b6sBgeW1JxSrntzHRmF5Y5tvl4WBnYMYVBcCEPiQxnQMRi/MwzZffjynny8MZWnv97DqgMnGP3cMny9LHQM9SU21Je4UF/iwnzpGOZHx1BfooN9mvX20eJdGdz3aRKllTbHNj8vC78YFMP0EXF0bX9hyyEE+niQGBPED+kFTHtnA1/97mKCfNWSUKu4opqSk//3vaM09YJIQymwSKtzac/2LNqVwVc/HOe3l3Rt0PDR5xfvI6OwnPgwXx66rCd9Y4IJ8/Nyaq4XD4uZGSPiGdoplNkLd7MhOYfSSht7M4rYm1F02v4Ws4lO4X4kxgRxWZ8OjOvZHnMT3Ub6esdxfvvxVuwGdAz1ZUTnMIZ2CmVi74hG6+tjMpl4a/pgrntzHam5pbz8/QEevSKhUY7dGoT7W+kZGcDejCIW7czghqEdXV2SSIuiieOk1ckvrWTkM99TWmkjwOpBn+ggIoO8aRdgJTrYhymJHQj3t572OrvdYOCTS8gvreI/d41gcHzoBdVRWllNRkE5R3JLScst5UhOzSMtt5QjuSWUV9WdybdzOz/uuKgz1w6OwbMR5+gwDIPxL6zgUHYJV/WP4vlr+zXpHCDf783ktrmbCfT2YPtjE93qlpirPfftXl5ddogBHYOZf9fIJguoIi2JJo6TNivY14vnr+3Hg/N/oKi8mnWHc+o8//TXexjZJYyJvSOZkBDhCC+1CzL6eFrOq+/LT/l6edC5nT+d2/mf9pxhGGQWVrAno5A1B07w2eY0DmeX8PCCHby96jBPX92XEV0aZ56O5fuzOZRdgtXDzBNX9WnyCctGdgkHoLC8muMF5UQF+zTp12tJbhjSkXfXpLAtNZ/1yTmO/ysRqZ8Ci7RKl/ftwISECPZlFLEvo4gTxRVkF1Ww8kA2+zOLWbYvm2X7snlkwQ6GxIfSKdyP706OlJnUO6JRWzjOxGQyERnkTWSQN5f0aM99E7ozb2Mqry8/RPKJEm5/bxPL/zCW9gHedV63P7OIL5OOsvNoIZGB3ozr1Z6LuoXj63XmH+VVB7K5b14SADcMiW2yod6nsp7SL8feOhpwG01sqC+T+0Ty+dajLN+XrcAi0gAKLNJqeVrMJ0fzBDm2GYbBrmOFrNifzTc7j7PzaCEbknPZcHLCtIhAKw9e1rPZa/W31kzffsPQjlz96hoOZBWzfG82150cTWIYBte9uY5NKXVnkP10cxrenmZGdgnH18tCZmE5eScnb6uotpGWWzMEe0DHYGZd3qtZ3ovJZCLUz4vckkrySqqI0QjnOsb2aM/nW4+y6sAJV5ci0qIosEibYjKZHCHmnku6kpZbypLdmeSXVtI9MoCxPdq7dNE+f6sHk/tEcuD7g7yx8hDjerXHAAY/udSxz/heEYzp0Y5DWcUs3ZNJel6ZYx6Vn/K0mJg2LI7fT+zerItEdm3vz8bkXLam5tE3Jqj+F7QhVdU1fZda2gSDIq6mwCJtWmyoL7dd1MnVZdRx1YBo5q5J4XB2CWOeW15nDZqekQG8c/Ngx+ePXZnArmOFbDmSR5XNTmSQt2NafJvdoHdUkEumyZ/cO5KNybm8vy6FacM6tomF/n5Iz2fumhS2p+c75re5ekA0wzqFEhnkTXmVnYNZRfxzdTIAF3XVWkIiDaFRQiJu6GBWEXe+v4XDJ2oufLGhPtw4NI7fjG0ZKyAXlldx8bPLKCirYmr/KGb/rDfBvq1zfSHDMPhoQyqz/7uLKptzv077RAfywW3DCNGaSyJOX78VWETcVJXNzg/p+RSWVzOic1iz3tJpDP/dfox7521zrPL8wITu3HFxp7N2EG6prnltDVtT8wEY17M904Z3pLTSxtc7jhMR6M2+jCLySqvw8TQT5m9lQq8IrhoQrVtCIicpsIiIy21OyeUXb6xzfB7q58X1Q2IZ0TkMf28PzCYTZhOYMGEy1XSU7tre3+3XYTpV/ENfAXBR13Dev22o5lYRaSAFFhFxC5XVdhZsS+e15Yc4klNa7/5xYb7MHNeNKxKjWkQrRG1g+d/vLqozIk1EnKOJ40TELXh5mLl+SEd+PjCGRbsyWLI7k93HCimvtmE/Odmv3TCwGwYFZVUcySnlgc+287fF+/nj5B78rF+U286WW15lw9NiospmqD+KSBNrUGB5/fXXef3110lJSQGgd+/e/PnPf+ayyy4DoLi4mIceeogvvviCnJwc4uPjuffee/nNb35z1mPOnTuXW2+99bTtZWVleHt7n+EVItISeVjMXJEYxRWJUWfdp7C8ig/WHeG9tSkczS9j5rwkDmUV88DEHs1YqfN2Hy+kymYQ7u9FVJB+X4k0pQYFlpiYGJ555hm6du0KwHvvvcfUqVPZtm0bvXv35v7772fZsmV8+OGHxMfHs3jxYu6++26ioqKYOnXqWY8bGBjIvn376mxTWBFpewK9Pbnnkq7cNqoTry0/yD++P8g/lh2kX2ww43pFuLq803y4/ggAQ+JD3bYVSKS1aNAN4iuvvJLLL7+c7t270717d5566in8/f1Zv349AOvWrePmm29m7NixxMfHc+edd9KvXz82b958zuOaTCYiIyPrPESk7fLxsvD7iT24cVhHDAN+89FWVh3IdnVZp1l2csK+GSPiXVuISBtw3j3abDYb8+bNo6SkhBEjRgBw0UUXsXDhQo4ePYphGCxbtoz9+/czadKkcx6ruLiYuLg4YmJiuOKKK9i2bVu9X7+iooLCwsI6DxFpXWb/rDcTEiKorLZz+3ub+XZXhqtLqqOgrGYZhM7t/FxciUjr1+DAsmPHDvz9/bFardx1110sWLCAhIQEAF5++WUSEhKIiYnBy8uLyZMn89prr3HRRRed9Xg9e/Zk7ty5LFy4kE8++QRvb29GjRrFgQMHzlnHnDlzCAoKcjxiY2Mb+lZExM15Wsy8euNAJvWuCS2//mALD3yaxM6jBa4ujdySSuwnx1h6tYGZfEVcrcHDmisrK0lNTSU/P5/58+fzzjvvsGLFChISEnj++ed5++23ef7554mLi2PlypXMmjWLBQsWMH78eKeOb7fbGThwIKNHj+bll18+634VFRVUVFQ4Pi8sLCQ2NlbDmkVaoWqbnSe/2sPctSmObX2jg7h5ZDxX9Y9yydT/T/5vN++sTqZvdBALfztKfVhEzlOzzcMyfvx4unTpwosvvkhQUBALFixgypQpjufvuOMO0tPTWbRokdPH/NWvfkV6ejrffPON06/RPCwird/W1DzeXZPCtzszqLTVjIm+tGd73pkxuNknbBvz3DKO5JTy5vRBTOqtfnci58vZ6/cF/1liGAYVFRVUVVVRVVWF2Vz3kBaLBXvtZAtOHi8pKYkOHTpcaGki0soM7BjCP345gPUPj+PByT3xtJj4fm8W/9txvFnr2Jicy5GcUrwsZkZ20SKGIs2hQcOaH374YS677DJiY2MpKipi3rx5LF++nEWLFhEYGMiYMWP4wx/+gI+PD3FxcaxYsYL333+fF154wXGMGTNmEB0dzZw5cwCYPXs2w4cPp1u3bhQWFvLyyy+TlJTEq6++2rjvVERajVA/L34ztguF5VW8vvwQi3dl8LN+Z5/fpbEUllex9UgejyzYCdSsxhzg7dnkX1dEGhhYMjMzmT59OsePHycoKIjExEQWLVrEhAkTAJg3bx6zZs1i2rRp5ObmEhcXx1NPPcVdd93lOEZqamqdVpj8/HzuvPNOMjIyCAoKYsCAAaxcuZKhQ4c20lsUkdbq4q7hvL78ENtOLj54vmx2g13HClhzMIe1h05wILOY3lGB+Ht7kF1UUfMoriC/tMrxmuhgHx6+vNcFvgMRcZbWEhKRFquwvIr+sxdjN2Dx/aPpHhHg1OtOFFewOSWX3ccK2XWskM1H8hxDlOsTE+LDxd3CuW98dyICNcGlyIXSWkIi0uoFensyvlcEi3dn8tD8H/jwjmH4ep3+a624opoNh3McLSh7M4pO2yfA6sGwzmGM6hpGz8hAdh0rwDCgXYCV9gHWmn8DvQny0S0gEVdQC4uItGjbUvO45vW1GAYEenswKC6EIB9PvD0t5JZUkppbyoGsYmz2ur/qekYGkBgTREKHQBJjg0mMDnLJ8GiRtq7ZhjW7CwUWkbZrzcETPPT5D6Tllp11n7gwX0Z2CWdU1zBGdA4jzN/ajBWKyNkosIhIm2K3G2w+ksfh7GKKyqspr7IR5OtJdLAPvToEEhXs4+oSReQM1IdFRNoUs9nE0E6hDO0U6upSRKQJ6IatiIiIuD0FFhEREXF7CiwiIiLi9hRYRERExO0psIiIiIjbU2ARERERt6fAIiIiIm5PgUVERETcngKLiIiIuD0FFhEREXF7CiwiIiLi9hRYRERExO0psIiIiIjbazWrNRuGAdQsUy0iIiItQ+11u/Y6fjatJrAUFRUBEBsb6+JKREREpKGKiooICgo66/Mmo75I00LY7XaOHTtGQEAARUVFxMbGkpaWRmBgoKtLkzMoLCzUOXJzOkfuT+fI/ekc1c8wDIqKioiKisJsPntPlVbTwmI2m4mJiQHAZDIBEBgYqG8QN6dz5P50jtyfzpH70zk6t3O1rNRSp1sRERFxewosIiIi4vZaZWCxWq089thjWK1WV5ciZ6Fz5P50jtyfzpH70zlqPK2m062IiIi0Xq2yhUVERERaFwUWERERcXsKLCIiIuL2FFhERETE7bW6wLJ//36mTp1KeHg4gYGBjBo1imXLltXZx2QynfZ44403XFRx2+PMOaqVk5NDTEwMJpOJ/Pz85i20DavvHOXk5DB58mSioqKwWq3Exsby29/+Vmt5NaP6ztH27dv55S9/SWxsLD4+PvTq1YuXXnrJhRW3Pc78rps5cyaDBg3CarXSv39/1xTaQrS6wDJlyhSqq6v5/vvv2bJlC/379+eKK64gIyOjzn7vvvsux48fdzxuvvlmF1Xc9jh7jgBuv/12EhMTXVBl21bfOTKbzUydOpWFCxeyf/9+5s6dy9KlS7nrrrtcXHnbUd852rJlC+3atePDDz9k165dPPLII8yaNYtXXnnFxZW3Hc78rjMMg9tuu43rr7/ehZW2EEYrkp2dbQDGypUrHdsKCwsNwFi6dKljG2AsWLDABRWKs+fIMAzjtddeM8aMGWN89913BmDk5eU1c7VtU0PO0aleeuklIyYmpjlKbPPO9xzdfffdxiWXXNIcJbZ5DT1Hjz32mNGvX79mrLDlaVUtLGFhYfTq1Yv333+fkpISqqurefPNN4mIiGDQoEF19v3tb39LeHg4Q4YM4Y033sBut7uo6rbF2XO0e/du/vKXv/D++++fczEsaXwN+TmqdezYMT7//HPGjBnTzNW2TedzjgAKCgoIDQ1txkrbrvM9R3IOrk5MjS09Pd0YNGiQYTKZDIvFYkRFRRnbtm2rs88TTzxhrF271ti2bZvx/PPPG76+vsYTTzzhmoLboPrOUXl5uZGYmGh88MEHhmEYxrJly9TC0syc+TkyDMO44YYbDB8fHwMwrrzySqOsrKz5i22jnD1HtdauXWt4enoaixcvbr4i27iGnCO1sNSvRfzp+vjjj5+xo+ypj82bN2MYBnfffTft27dn1apVbNy4kalTp3LFFVdw/Phxx/H+9Kc/MWLECPr378/vf/97/vKXv/Dcc8+58B22fI15jmbNmkWvXr246aabXPyuWpfG/jkC+Pvf/87WrVv54osvOHToEA888ICL3l3r0BTnCGDXrl1MnTqVP//5z0yYMMEF76z1aKpzJPVrEVPznzhxghMnTpxzn/j4eNasWcPEiRPJy8urs4x3t27duP3223nooYfO+No1a9Zw0UUXkZGRQURERKPW3lY05jnq378/O3bswGQyATWd0ux2OxaLhUceeYTZs2c36XtprZr652j16tVcfPHFHDt2jA4dOjRq7W1FU5yj3bt3c8kll3DHHXfw1FNPNVntbUVT/Rw9/vjjfPHFFyQlJTVF2a2Ch6sLcEZ4eDjh4eH17ldaWgpwWp8Hs9l8zj4q27Ztw9vbm+Dg4Auqsy1rzHM0f/58ysrKHM9t2rSJ2267jVWrVtGlS5dGrLptaeqfo9q/fSoqKi6gyratsc/Rrl27uPTSS7n55psVVhpJU/8cydm1iMDirBEjRhASEsLNN9/Mn//8Z3x8fHj77bdJTk5mypQpAPz3v/8lIyODESNG4OPjw7Jly3jkkUe48847tZpmM3DmHP00lNT+NdOrVy+FymbgzDn6+uuvyczMZMiQIfj7+7N7927++Mc/MmrUKOLj4137BtoAZ87Rrl27uOSSS5g4cSIPPPCAYyitxWKhXbt2riy/TXDmHAEcPHiQ4uJiMjIyKCsrc7SwJCQk4OXl5aLq3ZTLes80kU2bNhkTJ040QkNDjYCAAGP48OHG119/7Xj+m2++Mfr372/4+/sbvr6+Rp8+fYwXX3zRqKqqcmHVbUt95+in1Om2+dV3jr7//ntjxIgRRlBQkOHt7W1069bNePDBB3WOmlF95+ixxx4zgNMecXFxriu6jXHmd92YMWPOeJ6Sk5NdU7QbaxF9WERERKRtaxGjhERERKRtU2ARERERt6fAIiIiIm5PgUVERETcngKLiIiIuD0FFhEREXF7CiwiIiLi9hRYRERExO0psIiIiIjbU2ARERERt6fAIiIiIm5PgUVERETc3v8Di9xrzbrvpZsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"lets see if countyMAP is a multipolygon.  If so, we will take the biggest piece as the true MAP\")\n",
    "finalMAP = countyMAP\n",
    "if countyMAP.geom_type == \"MultiPolygon\":\n",
    "    print(\"by golly, it is!  I'll now plot the main piece of the map\")\n",
    "    maxArea = 0.\n",
    "    finalMAP = dummyPoly\n",
    "    for geom in countyMAP.geoms:\n",
    "        if geom.area > maxArea:\n",
    "            maxArea = geom.area\n",
    "            finalMAP = geom\n",
    "else:\n",
    "    print(\"Nope, it's a vanilla polygon.  stick with original county map\")\n",
    "plotPoly(finalMAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e5f3c139-e486-473a-8d97-1b00ec4f3430",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now build each county and precinct's list of neighbors, about 4 min for 9000 precincts\n",
      "done with county neighbors\n",
      "working on census vtd no 0\n",
      "working on census vtd no 400\n",
      "working on census vtd no 800\n",
      "working on census vtd no 1200\n",
      "working on census vtd no 1600\n",
      "working on census vtd no 2000\n",
      "working on census vtd no 2400\n",
      "working on census vtd no 2800\n",
      "working on census vtd no 3200\n",
      "working on census vtd no 3600\n",
      "working on census vtd no 4000\n",
      "working on census vtd no 4400\n",
      "working on census vtd no 4800\n",
      "working on census vtd no 5200\n",
      "working on census vtd no 5600\n",
      "working on census vtd no 6000\n",
      "working on census vtd no 6400\n",
      "working on census vtd no 6800\n",
      "working on census vtd no 7200\n",
      "working on census vtd no 7600\n",
      "working on census vtd no 8000\n",
      "working on census vtd no 8400\n",
      "working on census vtd no 8800\n",
      "all done finding each county's and each vtd's neighbors\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now build each county and precinct's list of neighbors, about 4 min for 9000 precincts\")      \n",
    "# start with county neighbor list for efficiency\n",
    "neighborCountyList = [-999]*nCounties\n",
    "for c in range(nCounties):\n",
    "    neighborCountyList[c] = []\n",
    "for c in range(nCounties):\n",
    "    for cc in range(c+1,nCounties):\n",
    "        if c != cc :  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"done with county neighbors\")\n",
    "\n",
    "neighborList = [[]]*nTracts\n",
    "for n in range(nTracts):\n",
    "    neighborList[n] = []\n",
    "for p in range(nTracts):\n",
    "    if p%400 == 0:\n",
    "        print(\"working on census vtd no\",p)\n",
    "    for pp in countyTractList[countyNo[p]] : #for efficiency, look only in home county and ...\n",
    "        if tractGeom[p].intersects(tractGeom[pp]) and p != pp:\n",
    "            neighborList[p].append(pp)\n",
    "    for cc in neighborCountyList[countyNo[p]]:  #... and neighboring counties\n",
    "        for pp in countyTractList[cc]:\n",
    "            if tractGeom[p].intersects(tractGeom[pp]):\n",
    "                neighborList[p].append(pp)\n",
    "print(\"all done finding each county's and each vtd's neighbors\")\n",
    "\n",
    "def isContiguous(TRACTLIST, NEIGHBORLIST):  #this function checks for contiguity\n",
    "    listLENGTH = len(TRACTLIST)\n",
    "    wasCounted = [0]*len(NEIGHBORLIST)\n",
    "    currentList = [TRACTLIST[0]]\n",
    "    countedThisRound = 1\n",
    "    wasCounted[TRACTLIST[0]] = 1\n",
    "    while np.sum(wasCounted) < listLENGTH and countedThisRound > 0:\n",
    "        countedThisRound = 0\n",
    "        newList = []\n",
    "        for T in currentList:\n",
    "            for TT in NEIGHBORLIST[T]:\n",
    "                if wasCounted[TT] == 0 and TT in TRACTLIST:\n",
    "                    countedThisRound += 1\n",
    "                    wasCounted[TT] = 1\n",
    "                    newList.append(TT)\n",
    "        currentList = newList\n",
    "    contiguity = False\n",
    "    if np.sum(wasCounted) == listLENGTH :\n",
    "        contiguity = True\n",
    "    if np.sum(wasCounted) > listLENGTH:\n",
    "        print(\"uh oh!  somehow we overcounted tractlist neighbors\")\n",
    "    return contiguity\n",
    "\n",
    "def addNeighbors(LIST, NEIGHBORLIST):\n",
    "    \"\"\"\n",
    "    This force-adds two (or more vtd's) to each other's neighbor lists.  Often used to overcome minor discontinuities\n",
    "    \"\"\"\n",
    "    for V in LIST:\n",
    "        for VV in LIST:\n",
    "            if V != VV and V not in NEIGHBORLIST[VV]:\n",
    "                NEIGHBORLIST[VV].append(V)\n",
    "    return NEIGHBORLIST            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2f621b6b-6658-4537-a133-0514c0b78c5f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets give the island precincts sufficient neighbors\n",
      "We have a lonely precinct no 988 ; its neighborList is [935]\n",
      "We have a lonely precinct no 1006 ; its neighborList is []\n",
      "We have a lonely precinct no 3750 ; its neighborList is [3741]\n",
      "[925, 926, 927, 933, 935, 937, 986, 988, 1006, 3619, 3620, 3621, 3623, 3624, 3625, 3638, 3737, 3740, 3741, 3742, 3744, 3749, 3750, 3753, 3754, 3755]\n",
      "I will manually add neighbor 926 to island 1006, pair 988 with 3619, pair 933 with 3750 \n"
     ]
    }
   ],
   "source": [
    "print(\"lets give the island precincts sufficient neighbors\")\n",
    "for p in range(nTracts):\n",
    "    nList = neighborList[p]\n",
    "    isSurroundedTract = \"no\"\n",
    "    if len(nList) == 1:  #checking if this tract is surrounded by a tract.  If so, a buffering will contain it\n",
    "        AREA = tractGeom[p].area\n",
    "        buffPoly = tractGeom[nList[0]].buffer(0.8*AREA**0.5)  #0.8 is conservative; will miss some surrounded ones\n",
    "        if buffPoly.contains(tractGeom[p]) :\n",
    "            isSurroundedTract = \"yes\"\n",
    "    if len(nList) < 2 and isSurroundedTract == \"no\" :\n",
    "            print(\"We have a lonely precinct no\",p,\"; its neighborList is\",neighborList[p])\n",
    "            #plotPoly(tractGeom[p])\n",
    "            #plotCenter(p,tractGeom[p])\n",
    "            # for pp in neighborList[p] :\n",
    "                # plotPoly(tractGeom[pp])\n",
    "            # plt.show()\n",
    "printList = []\n",
    "for t in range(nTracts):\n",
    "    if vtdCPx[t] <-82.7 and vtdCPx[t] > -82.85 and vtdCPy[t] > 41.35 and t not in skipList:\n",
    "        #plotPoly(tractGeom[t])\n",
    "        #plotCenter(t,tractGeom[t])\n",
    "        printList.append(t)\n",
    "#plt.show()\n",
    "print(printList)\n",
    "#for p in printList:\n",
    "#    print(p,neighborList[p])\n",
    "    \n",
    "print(\"I will manually add neighbor 926 to island 1006, pair 988 with 3619, pair 933 with 3750 \")\n",
    "neighborList[926].append(1006)\n",
    "neighborList[933].append(3750)\n",
    "neighborList[988].append(3619)  #Hey, this ain't Tampa Bay\n",
    "neighborList[1006].append(926)\n",
    "neighborList[3619].append(988)\n",
    "neighborList[3750].append(933)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ac1e23c5-2f0b-466e-8179-dfcf36acf390",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now figure out which vtds (tracts) are on the map boundary.  About 20sec to run\n",
      "working on tract 0\n",
      "working on tract 2000\n",
      "working on tract 4000\n",
      "working on tract 6000\n",
      "working on tract 8000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now figure out which vtds (tracts) are on the map boundary.  About 20sec to run\")\n",
    "isMapBoundaryTract = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    if t%2000 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if tractGeom[t].intersects(finalMAP.exterior) and t not in skipList:\n",
    "        plotPoly(tractGeom[t])\n",
    "        isMapBoundaryTract[t] = 1\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05e892dc-783b-4e88-aa0e-011f0df938a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ready to draw 8941 Home Districts for a 99 -district OH map ( 119186.343 per district).  Map diagonal =  5.59267\n"
     ]
    }
   ],
   "source": [
    "MAP = finalMAP\n",
    "#nDistricts = 99   #OH legislative  #already defined above\n",
    "# avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "MAPdiagonal = ((MAPMaxY - MAPMinY)**2 + (MAPMaxX - MAPMinX)**2)**0.5\n",
    "maxR = MAPdiagonal\n",
    "MAPboundary = MAP.exterior\n",
    "print(\"ready to draw\",nTracts,\"Home Districts for a\",nDistricts,\"-district\",STATE,\n",
    "      \"map (\",r3(avgDistrictPop),\"per district).  Map diagonal = \",r5(maxR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3ee429df-90fd-4872-abe2-0ea6c0db9573",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now ID the counties in various sub-regions that can make blue districts\n",
      "region no, nDistricts, counties contained: r0 5 [47, 25, 34, 61, 86]\n",
      "region no, nDistricts, counties contained: r1 22 [3, 17, 27, 66, 75, 76, 77]\n",
      "region no, nDistricts, counties contained: r2 11.11 [24]\n",
      "region no, nDistricts, counties contained: r3 7 [30]\n",
      "region no, nDistricts, counties contained: r7 1 [4, 36, 52, 57, 63, 81, 83]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "region no, nDistricts, counties contained: r4 3 [46, 21, 38]\n",
      "region no, nDistricts, counties contained: r5 2 [20]\n",
      "region no, nDistricts, counties contained: r6 7 [56, 8, 11, 18, 28, 54, 67, 82]\n",
      "region no, nDistricts, counties contained: r8 2 [49, 14]\n",
      "region no, nDistricts, counties contained: r9 2 [42]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "counties which can contribute to non-red areas represent 72.995 total districts\n",
      "but the total number of drawn possibly non-red districts is 62.11\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now ID the counties in various sub-regions that can make blue districts\")\n",
    "NWList = [47]\n",
    "LorainList = [46, 21, 38]\n",
    "NEList = []\n",
    "Franklin = [24]\n",
    "Delaware = [20]\n",
    "DaytonList = [56]\n",
    "Hamilton = [30]\n",
    "MahoningList = [49,14]\n",
    "SEList = [4]\n",
    "LakeList = [42]\n",
    "regions = [NWList, NEList, Franklin, Hamilton,  LorainList, Delaware,  DaytonList,  SEList,MahoningList,   LakeList]\n",
    "regionName = [\"NW\", \"NE\", \"Franklin\",\"Hamilton\", \"N-Lorain\",\"Delaware\", \"Dayton\",  \"SE\", \"Mahoning\",   \"Lake\"]\n",
    "regionPlotList = [[0,1,2,3,7],[4,5,6,8,9] ]\n",
    "#regionName = [\"NW\", \"NE\", \"Franklin\",\"Hamilton\",\"N-Lorain\",\"Delaware\", \"Dayton\",  \"SE\"] #old list.  need to renumber regions\n",
    "region_nD = [5, 1+1+10+4+3+3, 11.11, 7, 3,2,7, 1, 2, 2]  #Asht-Geau+SCG+Cuya+Summit+TrumPort+Start\n",
    "nRegions = len(regions)\n",
    "for c in range(nCounties):\n",
    "    if c not in DaytonList and countyGeom[c].intersects(countyGeom[DaytonList[0]]):\n",
    "        DaytonList.append(c)\n",
    "    if c not in NWList and countyGeom[c].intersects(countyGeom[NWList[0]]) :\n",
    "        NWList.append(c)\n",
    "    if countyGeom[c].centroid.x > -81.8 and countyGeom[c].centroid.y > 40.7 :  #CHECK THAT THESE ARE CORRECT\n",
    "        if c not in MahoningList and c not in LakeList:\n",
    "            NEList.append(c)\n",
    "    if c not in SEList and countyGeom[c].intersects(countyGeom[SEList[0]]) :\n",
    "        SEList.append(c)\n",
    "\n",
    "regionCountyList = [list()]*nRegions\n",
    "for i in range(2):\n",
    "    for rNo in regionPlotList[i]:  #plotting these in a goofy order so they don't display overlapped\n",
    "        r = regions[rNo]\n",
    "        regionCountyList[rNo] = list()\n",
    "        plotCenter(regionName[rNo],countyGeom[r[0]])\n",
    "        plt.text(countyGeom[r[0]].centroid.x, countyGeom[r[0]].centroid.y - 0.15, region_nD[rNo],ha='center')\n",
    "        for c in r:\n",
    "            plotPoly(countyGeom[c])\n",
    "            regionCountyList[rNo].append(c)\n",
    "        print(\"region no, nDistricts, counties contained: r\"+str(rNo),region_nD[rNo],regionCountyList[rNo]) \n",
    "    plt.show() \n",
    "nonRedPop = 0.\n",
    "for rList in regionCountyList:\n",
    "    for c in rList:\n",
    "        nonRedPop += countyPop[c]\n",
    "print(\"counties which can contribute to non-red areas represent\",r3(99.*nonRedPop/statePop),\"total districts\")\n",
    "print(\"but the total number of drawn possibly non-red districts is\",np.sum(region_nD))\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9f435901-86e7-4139-8f30-ff427545db78",
   "metadata": {
    "tags": []
   },
   "source": [
    "#DO NOT USUALLY RUN THIS BLOCK; SKIP  ****************\n",
    "print(\"Lets write the tract Biden and Trump pops and (vtd) neighbor lists to a file before we start\")\n",
    "date = \"08Oct\"\n",
    "tractNo = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\":tractNo,\"centroid x\":tractCPx,\"centroid y\":tractCPy,\"tractPop\":tractPop,\n",
    "                   \"countyNo\":countyNo,\"vtdBiden\":vtdBiden,\"vtdTrump\":vtdTrump,\"neighborList\":neighborList} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"vtd_input_data.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2290331d-2647-4a58-8ae1-2652cb7e185e",
   "metadata": {
    "tags": []
   },
   "source": [
    "#NO LONGER NEED TO RUN THE BELOW -- WE USED THIS TO CREATE LEGISLATIVE VERSION OF MUNI STATS 2/23/23\n",
    "print(\"Now, build the municipalities, their pops, neighborlists, etc.\")\n",
    "mName = \"OH04JanMunistatsUnmodified.csv\"  #these don't implement any big-city splits\n",
    "muniDF = pd.read_csv(\"state_HD_output/\"+mName)\n",
    "muniPop = muniDF['muniPop']\n",
    "muniPop = muniPop.tolist()  #avoids crankiness when I later splinter munis\n",
    "nMunis = len(muniPop)\n",
    "muniTLS = muniDF['muniTractList']\n",
    "muniNLS = muniDF['neighborList']   #muni neighborList string\n",
    "muniTractList =    [list() for i in range(nMunis)]\n",
    "muniNeighborList = [list() for i in range(nMunis)]\n",
    "muniPCPx = muniDF['centroid x']\n",
    "muniPCPy = muniDF['centroid y']\n",
    "muniPCP = [Point(muniPCPx[i], muniPCPy[i]) for i in range(nMunis) ]\n",
    "muniGeom = [muniPCP[i] for i in range(nMunis)]\n",
    "muniLean = [0.6]*nMunis\n",
    "tractMuniNo = [-999]*nTracts\n",
    "for m in range(nMunis):\n",
    "    muniNeighborList[m] = list()\n",
    "    if muniNLS[m] != \"[]\":\n",
    "        muniNeighborList[m] = ast.literal_eval(muniNLS[m])\n",
    "        \n",
    "for m in range(nMunis):\n",
    "    muniTractList[m] = list()\n",
    "    if muniTLS[m] != \"[]\":\n",
    "        muniTractList[m] = ast.literal_eval(muniTLS[m])\n",
    "        mTrumpers = 0\n",
    "        mBideners = 0\n",
    "        for v in muniTractList[m]:\n",
    "            tractMuniNo[v] = m\n",
    "            muniGeom[m] = muniGeom[m].union(tractGeom[v])\n",
    "            mTrumpers += vtdTrump[v]\n",
    "            mBideners += vtdBiden[v]\n",
    "        if mBideners + mTrumpers > 0:\n",
    "            muniLean[m] = mTrumpers / (mTrumpers + mBideners)\n",
    "countyMuniList = [list() for c in range(nCounties)]\n",
    "muniCountyNo = [-999]*nMunis\n",
    "for m in range(nMunis):\n",
    "    if muniTractList[m] != list():\n",
    "        c = countyNo[muniTractList[m][0] ]\n",
    "        muniCountyNo[m] = c\n",
    "        countyMuniList[c].append(m)\n",
    "\n",
    "countyPrintList = [17,76,42,49]  #pick some counties to plot cities\n",
    "for c in countyPrintList:\n",
    "    for m in range(nMunis):\n",
    "        if muniCountyNo[m] == c:\n",
    "            plotPoly(muniGeom[m])\n",
    "    plt.show()\n",
    "#plotting some of these counties to compare to Dave's Redistricting\n",
    "#there are a few spots where I collapse City + City Heights or Hills into one city.  this means I avoid some allowed splits. oh well."
   ]
  },
  {
   "cell_type": "raw",
   "id": "db016cb1-7bc0-4a25-af32-665760ce00b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "#NO LONGER NEED TO RUN THE BELOW -- WE USED THIS TO CREATE LEGISLATIVE VERSION OF MUNI STATS 2/23/23\n",
    "print(nMunis)\n",
    "print(\"here is a scatter of muni lean by muni pop\")\n",
    "plt.scatter(muniPop,muniLean)\n",
    "plt.xscale('log')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ed30941a-f715-4df4-8fd2-32fa857e42d6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#NO LONGER NEED TO RUN THE BELOW -- WE USED THIS TO CREATE LEGISLATIVE VERSION OF MUNI STATS 2/23/23\n",
    "origMuniPop = muniPop.copy()\n",
    "n_origMunis = nMunis\n",
    "keepWholeMuni = [0]*nMunis\n",
    "isBigMuni = [False]*nMunis\n",
    "print(\"Now splinter all the cities > 1.0d; no split restrictions on these in this code\")\n",
    "bigCityList = list()\n",
    "for m in range(nMunis):\n",
    "    if muniPop[m] > 1.05 * avgDistrictPop:\n",
    "        bigCityList.append(m)\n",
    "for m in range(n_origMunis):\n",
    "    if m in bigCityList:  #isBigMuni[m]:  #only splinter these explicit big cities\n",
    "        bigMuniPop = muniPop[m]\n",
    "        c = muniCountyNo[m]\n",
    "        bigCityTractList = muniTractList[m].copy()\n",
    "        nBigCityVTDs = len(muniTractList[m])\n",
    "        isBigMuni[m] = False  # this is redundant\n",
    "        muniNeighborList[m] = list()  #will rebuild below for each sub-muni\n",
    "        for mm in muniNeighborList[m].copy():\n",
    "            muniNeighborList[mm].remove(m)\n",
    "        mT0 = bigCityTractList[0]  #0th tract in Columbus's muniTractList will take place of existing Columbus\n",
    "        muniGeom[m] = tractGeom[mT0]\n",
    "        muniPop[m] = tractPop[mT0]\n",
    "        muniTractList[m] = [mT0]\n",
    "        #muniNameList[m] = muniNameList[m]+\"_0th_vtd\"\n",
    "        tractMuniNo[mT0] = m  #already true, but to be consistent ...\n",
    "        for M in range(nMunis,nMunis+nBigCityVTDs - 1):\n",
    "            cListNo = M - nMunis + 1  #the index number of the current bigCity vtd we are carving into a new muni\n",
    "            vNo = bigCityTractList[cListNo]\n",
    "            muniGeom.append(tractGeom[vNo])\n",
    "            muniPop.append(tractPop[vNo])\n",
    "            muniTractList.append([vNo])\n",
    "            tractMuniNo[vNo] = M\n",
    "            muniCountyNo.append(c)\n",
    "            countyMuniList[c].append(M)\n",
    "            muniNeighborList.append([])\n",
    "            keepWholeMuni.append(0)\n",
    "            isBigMuni.append(False)\n",
    "            #swallowedMuni.append(0)\n",
    "            #muniNameList.append(\"c\"+str(c)+\"bigMuni_vtd\"+str(cListNo))\n",
    "        for vNo in bigCityTractList:\n",
    "            mmm = tractMuniNo[vNo]\n",
    "            for mm in countyMuniList[c]:\n",
    "                if mm != mmm and muniGeom[mm].intersects(muniGeom[mmm]):\n",
    "                    if mm not in muniNeighborList[mmm]: #(watching for duplicates; new 3/15/23)\n",
    "                        muniNeighborList[mmm].append(mm)\n",
    "                    if mmm not in muniNeighborList[mm]:\n",
    "                        muniNeighborList[mm].append(mmm)\n",
    "            for cc in neighborCountyList[c]:               \n",
    "                for mm in countyMuniList[cc]:\n",
    "                    if mm != mmm and muniGeom[mm].intersects(muniGeom[mmm]):  #this OK; will not create duplication ###\n",
    "                        muniNeighborList[mmm].append(mm)\n",
    "                        muniNeighborList[mm].append(mmm)\n",
    "        print(\"Here are the munis in county\",c,\"including each vtd-muni from city\",m,\"with pop\",bigMuniPop)\n",
    "        for MM in countyMuniList[c]:\n",
    "            plotPoly(muniGeom[MM])\n",
    "        plt.show()\n",
    "        nMunis = len(muniGeom)  #need to update here if we have multiple big cities to splinter\n",
    "print(\"This splintering process resulted in\",nMunis,\"total munis vs. pre-splinter total\",n_origMunis)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "f80f4c71-510d-4889-a373-e18bd58dfd84",
   "metadata": {
    "tags": []
   },
   "source": [
    "#ANOTHER SKIP BLOCK\n",
    "print(\"we will now check for surrounded muni's, which we will collapse into their surrounders\")\n",
    "#print(\"this snippet is from OH_Congr_multisnap\")\n",
    "swallowedMuni = [0]*nMunis\n",
    "for m in range(nMunis):\n",
    "    if muniNeighborList[m] == 0:\n",
    "        print(\"uh oh! the following muni,pop has no neighbors\",m,muniPop[m])\n",
    "    if len(muniNeighborList[m])  == 1:\n",
    "        nL = muniNeighborList[m]\n",
    "        mm = nL[0]\n",
    "        #print(\"muni\",m,muniPop[m],\"is surrounded by muni\",mm,muniPop[mm],\"in county\",muniCountyNo[m],muniCountyNo[mm])\n",
    "        muniPop[mm] = muniPop[mm] + muniPop[m]\n",
    "        muniPop[m] = 0.\n",
    "        for tt in muniTractList[m]:\n",
    "            tractMuniNo[tt] = mm  \n",
    "        muniTractList[mm] = muniTractList[mm] + muniTractList[m]\n",
    "        muniTractList[m] = list()\n",
    "        muniNeighborList[m] = list()\n",
    "        muniNeighborList[mm].remove(m)\n",
    "        swallowedMuni[m] = 1\n",
    "\n",
    "print(\"I emptied\",np.sum(swallowedMuni),\"munis that were surrounded by a single other municipality\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3b6b533f-4a1f-4882-bf92-41b2ba9601b2",
   "metadata": {
    "tags": []
   },
   "source": [
    "#ANOTHER SKIP BLOCK\n",
    "print(\"oops, the above did not update the muniGeoms.  Let us do that\")\n",
    "for m in range(nMunis):\n",
    "    if muniPop[m] > 0 :   #we won't updated the emptied / surrounded munis\n",
    "        MTL = muniTractList[m]\n",
    "        muniGeom[m] = tractGeom[MTL[0]]\n",
    "        for v in MTL:\n",
    "            muniGeom[m] = muniGeom[m].union(tractGeom[v])"
   ]
  },
  {
   "cell_type": "raw",
   "id": "42cc0b34-634f-43d3-8efc-e7022ddc0f82",
   "metadata": {
    "tags": []
   },
   "source": [
    "#ANOTHER SKIP (initialIZING MUNI STATS FOR LEGISLATIVE prior to fragmented-township splitting)\n",
    "print(\"let us write muni stats (county, pop, vtdList, neighborList) to a file\")\n",
    "date = \"15Mar\"  #23Feb file has duplicates; #15Mar file did not split out vtds in discontiguous muni's\n",
    "finalMuniNo = [i for i in range(nMunis)]\n",
    "muniX = [muniGeom[i].centroid.x for i in range(nMunis)]\n",
    "muniY = [muniGeom[i].centroid.y for i in range(nMunis)]\n",
    "df = pd.DataFrame( {\"muniNo\":finalMuniNo,\"muniPop\":muniPop,\"countyNo\":muniCountyNo,\n",
    "                    \"centroid x\":muniX,\"centroid y\":muniY,\"muniTractList\":muniTractList,\"neighborList\":muniNeighborList} ) \n",
    "\n",
    "outname = STATE+date+\"muniStats_splitBig6.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "93f93ba3-9c4f-40f5-9f26-4d370c37c9b6",
   "metadata": {
    "tags": []
   },
   "source": [
    "#FOR COLD RE-START; READ IN THE ABOVE MUNI STATS AND CREATE THE MUNI LEANS - can now skip this block\n",
    "#  3/12/23 -- could add vtd neighbors 3087 and 3145 near Toledo.  I've fixed duplication in bigCity nLists\n",
    "print(\"Now, pull in the municipalities, their pops, neighborlists, centerpoints (prior to discontig muni fragmenting).  Build muni 2020 leans\")\n",
    "mName = \"OH15MarmuniStats_splitBig6.csv\"  #these split Ohio's big cities into vtd-muni's\n",
    "muniDF = pd.read_csv(\"state_HD_output/\"+mName)\n",
    "muniPop = muniDF['muniPop']\n",
    "muniCountyNo = muniDF['countyNo'].to_list()\n",
    "muniPop = muniPop.to_list()  #avoids any later crankiness\n",
    "nMunis = len(muniPop)\n",
    "muniTLS = muniDF['muniTractList']\n",
    "muniNLS = muniDF['neighborList']   #muni neighborList string\n",
    "muniTractList =    [list() for i in range(nMunis)]\n",
    "muniNeighborList = [list() for i in range(nMunis)]\n",
    "muniPCPx = muniDF['centroid x'].to_list()\n",
    "muniPCPy = muniDF['centroid y'].to_list()  #so we can append later\n",
    "muniPCP = [Point(muniPCPx[i], muniPCPy[i]) for i in range(nMunis) ]\n",
    "muniGeom = [muniPCP[i] for i in range(nMunis)]\n",
    "muniLean = [0.6]*nMunis\n",
    "tractMuniNo = [-999]*nTracts\n",
    "for m in range(nMunis):\n",
    "    muniNeighborList[m] = list()\n",
    "    if muniNLS[m] != \"[]\":\n",
    "        muniNeighborList[m] = ast.literal_eval(muniNLS[m])\n",
    "        \n",
    "for m in range(nMunis):  #rebuilding the muni geoms and tract lists from their component vtd's, calc muni Leans\n",
    "    muniTractList[m] = list()\n",
    "    if muniTLS[m] != \"[]\":\n",
    "        muniTractList[m] = ast.literal_eval(muniTLS[m])\n",
    "        mTrumpers = 0\n",
    "        mBideners = 0\n",
    "        for v in muniTractList[m]:\n",
    "            tractMuniNo[v] = m\n",
    "            muniGeom[m] = muniGeom[m].union(tractGeom[v])\n",
    "            mTrumpers += vtdTrump[v]\n",
    "            mBideners += vtdBiden[v]\n",
    "        if mBideners + mTrumpers > 0:\n",
    "            muniLean[m] = mTrumpers / (mTrumpers + mBideners)\n",
    "countyMuniList = [list() for c in range(nCounties)]\n",
    "for m in range(nMunis):\n",
    "    if muniTractList[m] != list():\n",
    "        c = muniCountyNo[m]\n",
    "        countyMuniList[c].append(m)\n",
    "\n",
    "countyPrintList = [46]  #pick some counties to plot cities, for funsies\n",
    "for c in countyPrintList:\n",
    "    for m in countyMuniList[c]:\n",
    "        plotPoly(muniGeom[m])\n",
    "    plt.show()\n",
    "\n",
    "keepWholeMuni = [0]*nMunis\n",
    "for m in range(nMunis):\n",
    "    if muniPop[m] > 0.5 * avgDistrictPop and muniPop[m] < 1.05 * avgDistrictPop:\n",
    "        keepWholeMuni[m] = 1\n",
    "        print(\"muni\",m,\"in county\",muniCountyNo[m],\"with pop\",muniPop[m],\"must be kept whole\")"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3ebe70b0-31cd-49f9-89c5-57e7752d9a75",
   "metadata": {
    "tags": []
   },
   "source": [
    "#new 3/17/23 - splitting discontiguous munis  -CAN SKIP THIS BLOCK FROM NOW ON\n",
    "splitMuniList = list()\n",
    "for m in range(nMunis):\n",
    "    if len(muniTractList[m]) > 1:\n",
    "        if not isContiguous(muniTractList[m],neighborList):\n",
    "            splitMuniList.append(m)\n",
    "            #print(\"muni\",m,\"is not contiguous!\")\n",
    "            #for v in muniTractList[m]:\n",
    "            #    plotPoly(tractGeom[v])\n",
    "            #    plotCenter(v, tractGeom[v])\n",
    "            #plotPoly(countyGeom[countyNo[v]])\n",
    "            #plt.show()\n",
    "origMuniPop = muniPop.copy()\n",
    "n_origMunis = nMunis\n",
    "print(\"Now fragment all\",len(splitMuniList),\" discontinuous munis\")\n",
    "\n",
    "alteredMuniList = splitMuniList.copy()  #these will flag the muniGeoms we need to rebuild\n",
    "for m in splitMuniList: \n",
    "    #plotPoly(muniGeom[m].buffer(0.01))\n",
    "    #plotPoly(tractCP[muniTractList[m][0]].buffer(0.01) )\n",
    "    nFragments = 0\n",
    "    splitMuniPop = muniPop[m]\n",
    "    c = muniCountyNo[m]\n",
    "    splitMuniTractList = muniTractList[m].copy()\n",
    "    nSplitMuniVTDs = len(muniTractList[m])\n",
    "    oldMuniNeighborList = muniNeighborList[m].copy()\n",
    "    muniNeighborList[m] = list()  #we will rebuild below for each sub-muni\n",
    "    for mm in muniNeighborList[m].copy():\n",
    "        muniNeighborList[mm].remove(m)\n",
    "    mT0 = splitMuniTractList[0]  #we build contiguous blocks starting with 0th vtd in original muni list\n",
    "\n",
    "    assignedList = []\n",
    "    unassignedList = splitMuniTractList.copy()\n",
    "    workingMuniNo = m\n",
    "    loopNo = 0\n",
    "    maxLoopNo = 20\n",
    "    while len(assignedList) < nSplitMuniVTDs and loopNo < maxLoopNo:\n",
    "        loopNo +=1\n",
    "        nFragments +=1\n",
    "        prevList = [ unassignedList[0] ]\n",
    "        contiguousList = [ unassignedList[0] ]\n",
    "        unassignedList.remove(unassignedList[0])\n",
    "        while prevList != list():  #still finding neighbors upon neighbors\n",
    "            newList = list()\n",
    "            for v in prevList:\n",
    "                for nV in neighborList[v]:\n",
    "                    if nV in unassignedList.copy() and nV not in contiguousList:\n",
    "                        contiguousList.append(nV)\n",
    "                        newList.append(nV)\n",
    "                        unassignedList.remove(nV)\n",
    "            prevList = newList.copy()\n",
    "        assignedList += contiguousList\n",
    "        #print(\"loop, assigned, unassigned\",loopNo,assignedList, unassignedList)\n",
    "        \n",
    "        if mT0 in contiguousList:  #this is our first fragment ...\n",
    "            newMuniNo = m\n",
    "        else:\n",
    "            newMuniNo = nMunis\n",
    "            nMunis +=1\n",
    "        newPop = 0.\n",
    "        newGeom = tractGeom[contiguousList[0]]\n",
    "        newPCP = get_PCP(contiguousList,tractCP, tractPop)\n",
    "        for v in contiguousList:\n",
    "            newPop += tractPop[v]\n",
    "            newGeom = newGeom.union(tractGeom[v])\n",
    "            tractMuniNo[v] = newMuniNo\n",
    "        #plotPoly(newGeom)\n",
    "        #plotCenter(nFragments, newGeom)\n",
    "        newNeighborList = list()\n",
    "        if mT0 in contiguousList:  #this is our first fragment ...\n",
    "            muniTractList[newMuniNo] = contiguousList.copy()\n",
    "            muniPop[newMuniNo] = newPop\n",
    "            muniGeom[newMuniNo] = newGeom\n",
    "            muniNeighborList[newMuniNo] = list()\n",
    "            muniPCP[m] = newPCP\n",
    "            muniPCPx[m], muniPCPy[m] = newPCP.x, newPCP.y\n",
    "        else:\n",
    "            muniTractList.append(contiguousList)\n",
    "            muniPop.append(newPop)\n",
    "            muniGeom.append(newGeom)\n",
    "            muniNeighborList.append([])\n",
    "            countyMuniList[c].append(newMuniNo)  #unlike 1st fragment, need to add to this list\n",
    "            muniCountyNo.append(c)\n",
    "            muniPCP.append(newPCP)\n",
    "            muniPCPx.append(newPCP.x)\n",
    "            muniPCPy.append(newPCP.y)\n",
    "        for MM in oldMuniNeighborList:\n",
    "            if muniGeom[MM].intersects(newGeom):\n",
    "                muniNeighborList[MM].append(newMuniNo)\n",
    "                muniNeighborList[newMuniNo].append(MM)\n",
    "    #plt.show()        \n",
    "    #print(\"we split muni\",m,\"in county\",c,\"into\",nFragments,\"fragments.  Total munis now =\",nMunis)\n",
    "print(\"This fragmenting process resulted in\",nMunis,\"total munis vs. pre-splinter total\",n_origMunis)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "20ee3a59-2f21-47ff-be2e-77daf52e5ec6",
   "metadata": {
    "tags": []
   },
   "source": [
    "countyPrintList = [46]  #showing that we split the discontig city S-E of Lorain\n",
    "for c in countyPrintList:\n",
    "    for m in countyMuniList[c]:\n",
    "        plotPoly(muniGeom[m])\n",
    "        plotCenter(m,muniGeom[m])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e6b6ec15-066d-4b55-b385-039216f2c634",
   "metadata": {
    "tags": []
   },
   "source": [
    "#last skip block\n",
    "print(\"let us write muni stats (county, pop, vtdList, neighborList) to a file\")\n",
    "date = \"17Mar\"  #23Feb file has duplicates; #15Mar file did not split out vtds in discontiguous muni's\n",
    "finalMuniNo = [i for i in range(nMunis)]\n",
    "muniX = [muniGeom[i].centroid.x for i in range(nMunis)]\n",
    "muniY = [muniGeom[i].centroid.y for i in range(nMunis)]\n",
    "df = pd.DataFrame( {\"muniNo\":finalMuniNo,\"muniPop\":muniPop,\"countyNo\":muniCountyNo,\n",
    "                    \"centroid x\":muniX,\"centroid y\":muniY,\"muniTractList\":muniTractList,\"neighborList\":muniNeighborList} ) \n",
    "\n",
    "outname = STATE+date+\"muniStats_splitBig6FragMunis.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eadda475-2513-4fe6-885f-1d94be1601dd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, pull in the municipalities, their pops, neighborlists, centerpoints.  Build muni 2020 leans\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "muni 3 in county 8 with pop 63399.0 must be kept whole\n",
      "muni 382 in county 46 with pop 65211.0 must be kept whole\n",
      "muni 484 in county 30 with pop 60424.0 must be kept whole\n",
      "muni 534 in county 49 with pop 60068.0 must be kept whole\n",
      "muni 1121 in county 17 with pop 81146.0 must be kept whole\n",
      "muni 1353 in county 8 with pop 62287.0 must be kept whole\n",
      "muni 1708 in county 75 with pop 82774.0 must be kept whole\n"
     ]
    }
   ],
   "source": [
    "#FOR COLD RE-START; READ IN THE ABOVE MUNI STATS AND CREATE THE MUNI LEANS - after above fragmenting of discontig munis\n",
    "#  3/17/23 -- could add vtd neighbors 3087 and 3145 near Toledo.  \n",
    "print(\"Now, pull in the municipalities, their pops, neighborlists, centerpoints.  Build muni 2020 leans\")\n",
    "mName = \"OH17MarmuniStats_splitBig6FragMunis.csv\"  #these split Ohio's big cities into vtd-muni's\n",
    "muniDF = pd.read_csv(\"state_HD_output/\"+mName)\n",
    "muniPop = muniDF['muniPop']\n",
    "muniCountyNo = muniDF['countyNo']\n",
    "muniCountyNo = muniCountyNo.to_list()\n",
    "muniPop = muniPop.tolist()  #avoids any later crankiness\n",
    "nMunis = len(muniPop)\n",
    "muniTLS = muniDF['muniTractList']\n",
    "muniNLS = muniDF['neighborList']   #muni neighborList string\n",
    "muniTractList =    [list() for i in range(nMunis)]\n",
    "muniNeighborList = [list() for i in range(nMunis)]\n",
    "muniPCPx = muniDF['centroid x']\n",
    "muniPCPy = muniDF['centroid y']\n",
    "muniPCP = [Point(muniPCPx[i], muniPCPy[i]) for i in range(nMunis) ]\n",
    "muniGeom = [muniPCP[i] for i in range(nMunis)]\n",
    "muniLean = [0.6]*nMunis\n",
    "tractMuniNo = [-999]*nTracts\n",
    "for m in range(nMunis):\n",
    "    muniNeighborList[m] = list()\n",
    "    if muniNLS[m] != \"[]\":\n",
    "        muniNeighborList[m] = ast.literal_eval(muniNLS[m])\n",
    "        \n",
    "for m in range(nMunis):  #rebuilding the muni geoms and tract lists from their component vtd's, calc muni Leans\n",
    "    muniTractList[m] = list()\n",
    "    if muniTLS[m] != \"[]\":\n",
    "        muniTractList[m] = ast.literal_eval(muniTLS[m])\n",
    "        mTrumpers = 0\n",
    "        mBideners = 0\n",
    "        for v in muniTractList[m]:\n",
    "            tractMuniNo[v] = m\n",
    "            muniGeom[m] = muniGeom[m].union(tractGeom[v])\n",
    "            mTrumpers += vtdTrump[v]\n",
    "            mBideners += vtdBiden[v]\n",
    "        if mBideners + mTrumpers > 0:\n",
    "            muniLean[m] = mTrumpers / (mTrumpers + mBideners)\n",
    "countyMuniList = [list() for c in range(nCounties)]\n",
    "for m in range(nMunis):\n",
    "    if muniTractList[m] != list():\n",
    "        c = muniCountyNo[m]\n",
    "        countyMuniList[c].append(m)\n",
    "\n",
    "countyPrintList = [17]  #pick some counties to plot cities, to show that fragmenting worked\n",
    "for c in countyPrintList:\n",
    "    for m in countyMuniList[c]:\n",
    "        plotPoly(muniGeom[m])\n",
    "        if muniPop[m] > 10000:\n",
    "            plotCenter(int(muniPop[m]),muniGeom[m])\n",
    "    plt.show()\n",
    "\n",
    "keepWholeMuni = [0]*nMunis\n",
    "for m in range(nMunis):\n",
    "    if muniPop[m] > 0.5 * avgDistrictPop and muniPop[m] < 1.05 * avgDistrictPop:\n",
    "        keepWholeMuni[m] = 1\n",
    "        print(\"muni\",m,\"in county\",muniCountyNo[m],\"with pop\",muniPop[m],\"must be kept whole\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "586a8618-d042-4392-999a-c267c85def5d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in the original (non-munified) HD angles and radii for all 8941 tracts\n",
      "working on Home District 0\n",
      "working on Home District 2000\n",
      "working on Home District 4000\n",
      "working on Home District 6000\n",
      "working on Home District 8000\n",
      "All 8941 home districts built for  OH\n"
     ]
    }
   ],
   "source": [
    "#pull in HD1 wedge shapes here for legislative map -- get from the VTD file so HDs and VTDs line up\n",
    "infilename = \"OH99HD1vtd09Oct.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "tractPop3 = df2['tractPop']  #these four may include shifting demography out of corners - avoid overwrite for now\n",
    "tractVAP = df2['tractVAP']   #OOPS!  I forgot to write tractVAP to pre-Oct my state HD output files!!\n",
    "#tractHisp = df2['tractHisp']  #OOPS\n",
    "#tractBlack = df2['tractBlack'] #OOPS\n",
    "# ... and now the true outputs\n",
    "HDvPop = df2['HD-pop']\n",
    "HDvHisp = df2['HDvHisp']\n",
    "HDvBlack = df2['HDvBlack']\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "HDangle0 = df2['HDangle0']\n",
    "HDangle1 = df2['HDangle1']\n",
    "HDangle2 = df2['HDangle2']\n",
    "HDangle3 = df2['HDangle3']\n",
    "HDradius0 = df2['HDradius0']\n",
    "HDradius1 = df2['HDradius1']\n",
    "HDradius2 = df2['HDradius2']\n",
    "HDradius3 = df2['HDradius3']\n",
    "HDcounty = df2['countyNo']\n",
    "startAngle = df2['startAngle']\n",
    "\n",
    "#tractLoopCounter = df2['Loops']\n",
    "HDNo = df2['tractNo']\n",
    "HDCPx = df2['centroid x']\n",
    "HDCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "nHDs = len(tractPop)  #the Home Districts were built off of precincts (but previously were done on blockgroups)\n",
    "print(\"I read in the original (non-munified) HD angles and radii for all\",nTracts,\"tracts\")\n",
    "#Now, recreate each Home District geometry\n",
    "dummyPoly = Polygon([ (0,0),(1,0),(1,1),(1,0)])\n",
    "dummyPoint = Point(0,0)\n",
    "HDPoly = [dummyPoly]*nHDs\n",
    "nWedges = 4\n",
    "HDangle = [0.]*nWedges\n",
    "HDradius = [0.]*nWedges\n",
    "Pt = [Point(0,0)]*(nWedges*2)\n",
    "#HDcounty = [-999]*nHDs\n",
    "countyHDList = [list() for c in range(nCounties)]\n",
    "for t in range(nHDs):  #Don't worry, we'll build trivial zero-area HD's for skipped tracts\n",
    "    if t%2000 == 0:\n",
    "        print(\"working on Home District\",t)\n",
    "    HDangle = [HDangle0[t],  HDangle1[t], HDangle2[t], HDangle3[t] ]\n",
    "    HDradius = [HDradius0[t], HDradius1[t], HDradius2[t], HDradius3[t] ]\n",
    "    #CP = Point( HDCPx[t],HDCPy[t] )\n",
    "    #c = 0\n",
    "    if tractPop3[t] > minTractPop:  #skip the unpopulated tracts for these lists\n",
    "        countyHDList[HDcounty[t]].append(t)\n",
    "    # while HDcounty[t] == -999 and c < nCounties:\n",
    "    #    if countyGeom[c].contains(CP) :\n",
    "    #        HDcounty[t] = c\n",
    "    #        countyHDList[c].append(t)\n",
    "    #    c +=1\n",
    "    if HDcounty[t] == -999:  #probably an island tract, just assign to closest county\n",
    "        print(\"tract\",t,\"at\",CP,\"might be an island tract\")\n",
    "        minDist = 1000.\n",
    "        for c in range(nCounties):\n",
    "            dist = countyGeom[c].distance(CP)\n",
    "            if dist < minDist:\n",
    "                HDcounty[t] = c\n",
    "                minDist = dist\n",
    "        print(\"we assigned it to county\",HDcounty[t])\n",
    "        countyHDList[c].append(t)\n",
    "    for nW in range(nWedges):\n",
    "        ccwAngle = HDangle[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle = HDangle[ int((nW+1)%4) ]\n",
    "        Pt[nW*2] = Point(HDCPx[t]+HDradius[nW]*math.cos(ccwAngle),\n",
    "                         HDCPy[t]+HDradius[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(HDCPx[t]+HDradius[nW]*math.cos(cwAngle),\n",
    "                           HDCPy[t]+HDradius[nW]*math.sin( cwAngle) )\n",
    "        \n",
    "    HDPoly[t] = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "print(\"All\",nHDs,\"home districts built for \",STATE)  \n",
    "isModifiedHD = [0]*nHDs  #we will flag HDs that need to be modified for county boundaries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b40ba439-296a-45a6-aa24-6c60ace0a16c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "county 24 is a whole county ( 11.107 ) with pop 1323807.0 = 11.11 districts.\n",
      "county 30 is a whole county ( 6.969 ) with pop 830639.0 = 6.97 districts.\n",
      "county 42 is a whole county ( 1.952 ) with pop 232603.0 = 1.95 districts.\n",
      "county 49 is a whole county ( 1.918 ) with pop 228614.0 = 1.92 districts.\n",
      "county 69 is a perfect county with pop 124936.0\n",
      "county 82 is a whole county ( 2.033 ) with pop 242337.0 = 2.03 districts.\n",
      "county 84 is a perfect county with pop 116894.0\n",
      "all done computing each county's population center\n"
     ]
    }
   ],
   "source": [
    "# Each HD is assigned to one of x types #NOTE, AS OF MARCH'23, THE \"TYPES\" ARE OBSOLETE BUT SOME OF THIS IS USED\n",
    "# A) \"perfect\"; HD boundary = county boundary.  Banned from other HDs\n",
    "# B) \"wholeDistrict\"; has an integer number of legislative districts, determined via piecut or HD1 within county. Banned from other HDs\n",
    "# C) \"openRange\"; can pick up pop from any unbanned county - these are not drawn as they are assuredly red\n",
    "# D) \"pairing\"; has <1 districts, helps a messy county square up by absorbing its remnant\n",
    "# E) messy counties; its HDs will be either \"inCounty\" or \"remnant\"; has non-integer >1 districts.  Most HDs are in-county, some pull in paired-county pop\n",
    "#    If \"inCounty\", all district pop must be harvested from in-county\n",
    "#    If \"remnant\", a specified fraction is in-county with remainder from counties in the \"pairingCounty\" list\n",
    "# Each HD in a pairing county is assigned \"inCounty\" or \"paired\" based on whether it absorbs a remnant\n",
    "#    If \"paired\", must absorb specified remnant before its own county, then openRange counties\n",
    "isOkHDTL3 = [True] *nHDs #will flag false if we cannot form a 1-munisplit HD from this starting centerpt\n",
    "HDtype = [\"openRange\"]*nHDs  #default type, minimal constraints\n",
    "bannedCounties = [[]]*nHDs  #list of counties that cannot contribute pop to this HD\n",
    "pairedCounties = [[]]*nHDs  #list of counties that MUST contribute some pop to this HD\n",
    "primaryPairedCounty = [-999]*nHDs  #the paired county we must draw from before filling remainder with other neighbor counties\n",
    "isPerfectCounty = [0]*nCounties  #exactly one district fits in this county\n",
    "isWholeCounty = [0]*nCounties   #an integer number of districts fits well in this county\n",
    "countyStraddlerList = [list() for c in range(nCounties)]  #for messy counties, this will store the list of tracts which center districts that straddle counties\n",
    "countyStraddlerHDList = [list() for c in range(nCounties)]  #and these are the lists of HDtractLists triggered by the straddlers (including their implicated whole districts)\n",
    "isPairingCounty = [0]*nCounties  #a county exceeding a full district, but not an integer number of districts (has a remnant partial)\n",
    "bannedVTDList = [list() for t in range(nHDs)]  #for HDs inside a county w paired remnant, we can ban non-remnant VTDs from remnant HDs and vice versa\n",
    "bannedCounties = [list() for t in range(nHDs)]  #useful for openRange tracts\n",
    "HDtractList = [list() for t in range(nHDs)] * nHDs  #the list of whole tracts in each HD after \"snapping\" to whole tracts from orig HD1 shape\n",
    "HDtractList3= [list() for t in range(nHDs)] * nHDs  #same, but snapping to munis rather than simply to vtd's\n",
    "HD_Choosers = [list() for t in range(nHDs)]*nHDs   #list of tracts which would select this independent HD\n",
    "HD_Choosers3 = [list() for t in range(nHDs)]*nHDs   #list of tracts which would select this independent muni-snapped HD\n",
    "planChoosers = list()   #list of HDs which prefer a regional plan (could be an anySplit or noSplit plan)\n",
    "chosenVTDplan = [ [-999]*nTracts for r in range(nRegions)]\n",
    "chosenMuniPlan = [ [-999]*nTracts for r in range(nRegions)]\n",
    "planRules = list()  #this will flag \"anySplit\" or \"noSplit\" based on which rule set used to generate a plan's districts\n",
    "districtTractList = list()  #list of tracts in a regionally-associated district, to be voted on\n",
    "#districtTractList3= list()\n",
    "planTLnos = list()  #list of dTL numbers associated with a regional plan\n",
    "planRegionNo = list()  #region associated with this regional plan\n",
    "chosenPlan = [-999]*nHDs    #regional anySplit plan that a particular tract would prefer\n",
    "chosenPlan3= [-999]*nHDs    #same, for muni-no-split plans\n",
    "needAltPlanList = list()  #temporary list of tracts who generated a regional plan that's discontiguous,\n",
    "#   so we'll later need to assign this tract an alternate regional plan\n",
    "GCP = [Point(0,0)]*nHDs  #GCP, PCP are a Home District's geographic and population center points\n",
    "PCP = [Point(0,0)]*nHDs\n",
    "districtPCP = list()   #for regionally-driven districts, we will create a separate list of their pop center points for easier cross-ref\n",
    "snappedHDpop = [0.] * nHDs  #the total pop in an HDtractList snapped to vtd's\n",
    "snappedHD3pop = [0.] * nHDs  #the total pop in an HDtractList snapped to muni's\n",
    "bigWholeList = []  #statewide list of tracts in big counties with integer number of districts\n",
    "\n",
    "for c in range(nCounties):\n",
    "    if countyPop[c] > 0.95 * avgDistrictPop and countyPop[c] < 1.05 * avgDistrictPop :\n",
    "        isPerfectCounty[c] = 1\n",
    "        isWholeCounty[c] = 1\n",
    "        print(\"county\",c,\"is a perfect county with pop\",countyPop[c])\n",
    "        for t in countyTractList[c]:\n",
    "            HDtype[t] = \"perfect\"\n",
    "            HDtractList[t] = countyTractList[c]\n",
    "            HDtractList3[t] = HDtractList[t].copy()\n",
    "            HD_Choosers[t] = [t]  #although all tracts chose the same 1-district plan, we count as if each tract specified its own plan\n",
    "            HD_Choosers3[t] = [t]\n",
    "    if countyPop[c] > 1.5 * avgDistrictPop: #exclude small counties\n",
    "        rcD = round(countyDistricts[c],0)\n",
    "        fracOffset = abs(countyDistricts[c] - rcD ) / rcD\n",
    "        if fracOffset < 0.05 -  min(0.03 , 0.002 * rcD) :  #exclude Stark w 3.145, Cuy 10.61 but include Franklin 11.11\n",
    "            isWholeCounty[c] = 1\n",
    "            print(\"county\",c,\"is a whole county (\",r3(countyDistricts[c]),\") with pop\",countyPop[c],\"=\",round(countyPop[c]/avgDistrictPop,2),\"districts.\")\n",
    "            for t in countyTractList[c]:\n",
    "                if countyDistricts[c] < 5.5:\n",
    "                    HDtype[t] = \"pieCut\"\n",
    "                else:\n",
    "                    HDtype[t] = \"bigWhole\"\n",
    "                    bigWholeList.append(t)  #list of tracts in large counties with integer number of districts\n",
    "    if countyPop[c] > 1.05 * avgDistrictPop and isWholeCounty[c] != 1:\n",
    "        isPairingCounty[c] = 1\n",
    "        for t in countyTractList[c]:\n",
    "            HDtype[t] = \"messy\"  #for now, will later be divided into inCounty and remnant\n",
    "for t in range(nHDs):  #now, we initialize a list of banned counties when building each HD, starting with whole counties outside this tract's county\n",
    "    c = HDcounty[t]\n",
    "    for cc in range(nCounties):\n",
    "        if isWholeCounty[cc] == 1 and c != cc:\n",
    "            bannedCounties[t].append(cc)\n",
    "\n",
    "def compactScore(SUBLIST, TRACTCP, TRACTPOP):\n",
    "    \"\"\"  #see e.g. Roland Fryer 2007 https://www.nber.org/system/files/working_papers/w13456/w13456.pdf\n",
    "    Simple code to compute pop-weighted root-mean-square distance of each person to their district's pop center pt\n",
    "    SUBLIST is the vtd no's in this district, TRACTCP is the list of vtd centerpoints\n",
    "    \"\"\"\n",
    "    sumSquareDist = 0.\n",
    "    totPop = 0.\n",
    "    sublistPCP = get_PCP(SUBLIST, TRACTCP, TRACTPOP)\n",
    "    for V in SUBLIST:\n",
    "        totPop += TRACTPOP[V]\n",
    "        sumSquareDist += TRACTPOP[V] * (TRACTCP[V].distance(sublistPCP) )**2\n",
    "    SCORE = (sumSquareDist / totPop) **0.5\n",
    "    return SCORE\n",
    "\n",
    "countyPCP = [Point(0,0)]*nCounties  #each county's population centerpoint\n",
    "for c in range(nCounties):\n",
    "    countyPCP[c] = get_PCP(countyTractList[c], tractCP, tractPop3)\n",
    "print(\"all done computing each county's population center\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "4fefeab6-3930-4197-8d4f-5bba8f30c7da",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 0.18117\n",
      "23 0.08757\n",
      "50 0.0853\n",
      "61 0.21236\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#test block for FryerHolden compactness scoring; ignore\n",
    "fig,ax = plt.subplots()\n",
    "for c in range(nCounties):\n",
    "    scor = compactScore(countyTractList[c],tractCP,tractPop3)\n",
    "    #print(c,countyPop[c],score)    \n",
    "    plt.scatter(countyGeom[c].area,scor)\n",
    "    if scor > 0.18 or scor < 0.1:\n",
    "        print(c,r5(scor))\n",
    "ax.set(xlabel=\"county area\")\n",
    "ax.set(ylabel=\"nonCompactness score\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "9b081dfb-61b4-4238-bcf1-35c43a83d569",
   "metadata": {},
   "source": [
    "# OBSOLETE!! Data structure:\n",
    "\n",
    "#COMPUTE POPULATION and GEOGRAPHIC CENTROIDS OF EACH DRAWN DISTRICT:  PCP[t] amd GCP[t]\n",
    "Each tract gets an HDtractList of block groups assigned to its post-snapped HD\n",
    "Ohio is divided into isolated blue/purple regions surrounded by red counties.\n",
    "  Aside from Hamilton and Franklin counties, which are self-contained maps, these regions straddle multiple counties.\n",
    "Each region of interest can be mapped using many regional plans of one or more districts.\n",
    "Each tract in a region develops an HDtractList, HDtractList3 that snaps to a list of tracts, respecting vtd, muni no-split rules.\n",
    "   For multi-district regions, this HDTL choice cascades into a regional plan.\n",
    "After all regional plans created, each regional tract votes for the plan that has a district that best centers that tract.\n",
    "...\n",
    "Each regional plan is thus weighted by the fraction of regional voters who support that plan.\n",
    "\n",
    "DETERMINING HDtractLists: (same general strategy for vtd-snapped vs. muni-snapped)\n",
    "Blank out nHDs's number of HDtractLists\n",
    "Fill in HDtLs for perfect counties and whole counties.Initialize the HDchoosers list for each HD with its home tract. \n",
    "   For large whole counties, use HD1 on the county; ignore initial HDPoly in drawing the snapped HD.  Each tract votes only on its HD.  \n",
    "   For 2-district whole counties, vary the angle of bisection to create 2-district plans\n",
    "   For counties with populations > 1 district, but not evenly divisible:\n",
    "      Find the \"straddler-initiating\" vtd's near the county perimeter that had the highest ex-county fraction of HDPoly population\n",
    "         For each straddler, create the in-county part of the district so that the excluded county pop is a whole number of districts\n",
    "           Complete the straddler district with suitable populations from an adjacent pairing county.\n",
    "             If that pairing county is also constrained by the county-split rule, complete the straddler district with a 3rd county\n",
    "           Bisect/trisect/quadrisect the main county's pop outside the whole district into whole districts\n",
    "             Secting technique will be variations of pizza-cutting based on the county geometry\n",
    "           Check the regional set of districts for contiguity and population.  If legit, add to list of qualifying regional plans\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2829be20-0515-4461-97db-b0f13c0b081c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#I think snapHD no longer used?\n",
    "def snapHD(t,TARGETPOP, TOLERPOP, allowedTractList,TRACTCP, TRACTPOP, ANGLE, CURRENTR, MAXR) : #this is the snapping function ...              \n",
    "    \"\"\"\n",
    "    this is the snapping function to generate a list of permissible tracts in an HD1 shape expanded or contracted to meet a target pop\n",
    "    a tract / vtd is captured if the expanded / shrunk shape contains the tract's centerpoint\n",
    "    the original polygon is passed as a list of four angles and radii to define its eight vertices\n",
    "    NOTE: Contiguity is NOT guaranteed -- USE A VTDSNAP CODE INSTEAD\n",
    "    \"\"\"\n",
    "    snappedPop = 0.\n",
    "    Rratio = 1.0  #ratio of radii to original HD1 radii\n",
    "    MAX_DR = MAXR / np.min(CURRENTR)  #working in ratios here.  MAXR is typically the county radius, while city-based radii can be smaller\n",
    "    snapLoopNo = 0\n",
    "    maxSnapLoopNo = 15\n",
    "    ANGLE2 = [ANGLE[1], ANGLE[2], ANGLE[3], ANGLE[0] ]\n",
    "    while abs(snappedPop - TARGETPOP) > TOLERPOP and snapLoopNo < maxSnapLoopNo:\n",
    "        SNAPPEDHDTRACTLIST = []\n",
    "        #dr = 1.0  # COMMENT THIS BACK OUT LATER ************\n",
    "        if snapLoopNo > 0:  #we don't start bisecting until after the first loop\n",
    "            dr = MAX_DR * np.sign(TARGETPOP - snappedPop)\n",
    "            Rratio = max( 0.5 * Rratio, Rratio + dr)\n",
    "            MAX_DR = MAX_DR * 0.5        \n",
    "        snappedPop = 0.  #reset this loop's county pop ensnared in the HDpolly\n",
    "        HDpolly = TRACTCP[t].buffer(0.001) #seeding this loop's expanded or contracted HD1 polygon shape           \n",
    "        for nW in range(4):  #draw each wedge, add to the HDpolly\n",
    "            cR = Rratio * CURRENTR[nW]\n",
    "            outerPt2 = Point(TRACTCP[t].x+cR*math.cos(ANGLE[nW]), tractCP[t].y+cR*math.sin(ANGLE[nW]) )\n",
    "            outerPt3 = Point(TRACTCP[t].x+cR*math.cos(ANGLE2[nW]),tractCP[t].y+cR*math.sin(ANGLE2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([TRACTCP[t],outerPt2,outerPt3]) )  #add this wedge to HD district polygon  \n",
    "        for tt in allowedTractList:\n",
    "            if HDpolly.contains(TRACTCP[tt]):\n",
    "                snappedPop += TRACTPOP[tt]\n",
    "                SNAPPEDHDTRACTLIST.append(tt)\n",
    "        snapLoopNo +=1\n",
    "    if snapLoopNo == maxSnapLoopNo:\n",
    "        print(\"Somehow, we did not converge on snapping HD\",t,\"to whole tracts. Final pop vs targetPop =\",r3(snappedPop),\"vs\",r3(TARGETPOP) )\n",
    "    return SNAPPEDHDTRACTLIST\n",
    "\n",
    "def getClosestTract(POINT, TRACTLIST, TRACTCP):\n",
    "    \"\"\"\n",
    "    Useful for orienting pizzacuts in partially used pairing counties\n",
    "    returns the vtd / tract index out of a curated TRACTLIST collection of indices that is closest to the POINT\n",
    "    The (x,y) points of these indices are in the TRACTCP list\n",
    "    \"\"\"\n",
    "    minDist = 99999.\n",
    "    for tt in TRACTLIST:\n",
    "        dist = POINT.distance(TRACTCP[tt])\n",
    "        if dist < minDist:\n",
    "            closestTract = tt\n",
    "            minDist = dist\n",
    "    return closestTract  #returns the tract no of a tractlist's closest tract to the specified point\n",
    "\n",
    "def getFarthestTract(POINT, TRACTLIST, TRACTCP):  #useful for orienting piecuts in partially used pairing counties\n",
    "    maxDist = 0.\n",
    "    for tt in TRACTLIST:\n",
    "        dist = POINT.distance(TRACTCP[tt])\n",
    "        if dist > maxDist:\n",
    "            farthestTract = tt\n",
    "            maxDist = dist\n",
    "    return farthestTract  #returns the tract no of a tractlist's closest tract to the specified point\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c6300f21-339e-4633-b152-22fa47aee558",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#more district-drawing sub-methods.  All seem to work\n",
    "def getDiag(CPLIST):\n",
    "    \"\"\"\n",
    "    quick function to compute the max diagonal of a box around a set of centerpoints (e.g. tract centerpoints)\n",
    "    \"\"\"\n",
    "    minX, maxX = CPLIST[0].x, CPLIST[0].x\n",
    "    minY, maxY = CPLIST[0].y, CPLIST[0].y\n",
    "    for t in CPLIST:\n",
    "        if t.x < minX:\n",
    "            minX = t.x\n",
    "        if t.x > maxX:\n",
    "            maxX = t.x\n",
    "        if t.y < minY:\n",
    "            minY = t.y\n",
    "        if t.y > maxY:\n",
    "            maxY = t.y\n",
    "    diag = ((maxY-minY)*(maxY-minY) + (maxX-minX)*(maxX-minX) ) ** 0.5\n",
    "    return diag  \n",
    "\n",
    "def cutMuniSnap(TARGPOP, TOLRPOP, MUNITRACTLIST,TCPLIST, SUBLIST, MUNINEIGHBORLIST, TRACTPOP, MUNIPOP, MUNIPCP, ANGLE, CUTPOINT,DEBUG=\"no\") : \n",
    "    \"\"\"\n",
    "    This is a modification to OH_patch_legisl_org's muniSnap, but simplified to have no \"big Munis\"; all have been splintered\n",
    "    The template shape is not an HDPoly, but an angled box with a leading edge, specified by a fixed angle and an original \"cutpoint\",\n",
    "    which is a point on the leading edge.  The main code sets the cutpoint, often the centerpoint of a list of tracts.\n",
    "    The box is big enough to capture all tract centerpoints on one side of the pizza cut.\n",
    "    The logic is generally the same as muniSnap, growing neighbor-by-neighbor (from a home muni far from the original cutpoint)\n",
    "    at each guessed cutpoint until all contiguous munis on the correct side of the leading edge are captured.\n",
    "    Essentially, we iterate on pizza slicing point at a fixed angle until we capture enough munis' pops to one side of the pizza slice\n",
    "    Capturing occurs if > 0.5 of the muniPop has vtd centerpoints inside the polygon\n",
    "    The population of captured munis is compared to the target.  If not within tolerance, we move the cutpoint via a bisection method\n",
    "    until we are within tolerance or we have maxed out the bisection counter\n",
    "    We pass back the captured tractList and the captured (\"snapped\") pop of these whole munis.\n",
    "    Inputs include a curated \"SUBLIST\" which only includes vtd nos w centerpoints within the counties we're allowed to draw from;\n",
    "     this allows us to set a \"home muni\" far from the cutpoint that is forced / known to be in the final tractList\n",
    "    \"\"\"\n",
    "    #first, set a goodsized polygon side length to capture all in tracts on 1 side of a pizza cut\n",
    "    subListTCPs = list()\n",
    "    for V in SUBLIST:\n",
    "        subListTCPs.append(TCPLIST[V])\n",
    "    sliceDiam = 1.5 *getDiag(subListTCPs)  #some buffer vs. a diagonal through the bounding box of all in-play tract centerpoints \n",
    "    origPolly = angledPentaPoly(CUTPOINT, ANGLE,sliceDiam)  \n",
    "    #define a \"home muni\" far from the original cutpoint on the correct side of the pizza cut\n",
    "    maxVtdDist = 0.\n",
    "    for V in SUBLIST:\n",
    "        if TCPLIST[V].distance(CUTPOINT) > maxVtdDist and origPolly.contains(TCPLIST[V]):\n",
    "            maxVtdDist = TCPLIST[V].distance(CUTPOINT)\n",
    "            for M in range(len(MUNITRACTLIST)):  #finding the home muni for the vtd we are forcing to be included in the munisnapped district\n",
    "                if V in MUNITRACTLIST[M]:\n",
    "                    HM = M\n",
    "    guessedCenter = CUTPOINT\n",
    "    CPx = CUTPOINT.x\n",
    "    CPy = CUTPOINT.y\n",
    "    maxLoopNo = 18\n",
    "    SNAPPOP = 0.\n",
    "    loopNo = 0\n",
    "    dx_dr = -1.*math.sin(ANGLE)  # when moving the cutpoint, we move it perpendicular to the cut angle, hence this reverses sin & cos\n",
    "    dy_dr = math.cos(ANGLE)\n",
    "    SIGN = -1.0   #I get confused about which way we should step the cutpoint.  -1 works with above dx_dr & dy_dr sign convention\n",
    "    abs_dr = 0.5*sliceDiam #initial step size\n",
    "    dr = 0.  #dr is the distance (toward our primary tract) from the PCP to the current guessed center   \n",
    "    FPTL, FPML = list(),list()  #these will become the lists of full vtd's/muni's in the last polygon to capture muni pop's under the target\n",
    "    SPLITMUNI = -999 #this will become the muni we reco to split-add to satisfy district Pop\n",
    "    while abs(SNAPPOP - TARGPOP) > TOLRPOP and loopNo < maxLoopNo:  #use bisection method to optimize slice point\n",
    "        if loopNo > 15 and DEBUG != \"no\":\n",
    "            print(loopNo,\"loop for cutMuniSnap, previous loop snapPop was\",SNAPPOP,\"our targ, tolr are\",int(TARGPOP), int(TOLRPOP))\n",
    "        loopNo +=1\n",
    "        sliceList = list()\n",
    "        if loopNo > 1:  #first loop, do not adjust\n",
    "            dr += abs_dr * SIGN*np.sign(TARGPOP - SNAPPOP)\n",
    "            guessedCenter = Point(CPx + dx_dr*dr, CPy + dy_dr*dr)\n",
    "            abs_dr = 0.5 * abs_dr  #stepsize change for next round\n",
    "        Polly = angledPentaPoly(guessedCenter, ANGLE,sliceDiam)\n",
    "        #plotPoly(Polly)\n",
    "        #plotCenter(loopNo, Polly)\n",
    "        #plotPoly(TCPLIST[MUNITRACTLIST[HM][0]].buffer(0.05))\n",
    "        #for ttt in SUBLIST:\n",
    "        #    plotPoly(TCPLIST[ttt].buffer(0.02))\n",
    "        #plt.show()\n",
    "        fullPollyTractList = []\n",
    "        latestPollyMuniList = [HM]\n",
    "        fullPollyMuniList = [HM]\n",
    "        fullPollyTractList = MUNITRACTLIST[HM].copy()   #all HDs absorb full muni if nonBig\n",
    "        rd = 0\n",
    "        while len(latestPollyMuniList) > 0 :  #i.e. while we are still finding neighbors of neighbors in the current Polly\n",
    "            if DEBUG != \"no\":\n",
    "                print(rd,loopNo,\",cutMS rd, loop. Last rd found=\",latestPollyMuniList,\"contiguous munis for tract\",t)\n",
    "            rd +=1\n",
    "            previousPollyMuniList = latestPollyMuniList.copy()\n",
    "            latestPollyMuniList = list()\n",
    "            for M in previousPollyMuniList:  #all muni's we have captured as of end of last round\n",
    "                for MM in MUNINEIGHBORLIST[M]:\n",
    "                    if MM not in fullPollyMuniList:\n",
    "                        capturedPop = 0.\n",
    "                        for TT in MUNITRACTLIST[MM]:\n",
    "                            if TT in SUBLIST and Polly.contains(TCPLIST[TT]) :\n",
    "                                capturedPop += TRACTPOP[TT]\n",
    "                        if capturedPop > 0.5 * MUNIPOP[MM]:\n",
    "                            latestPollyMuniList.append(MM)\n",
    "                            fullPollyMuniList.append(MM)\n",
    "                            fullPollyTractList = fullPollyTractList + MUNITRACTLIST[MM]\n",
    "                                    \n",
    "        # end of while to build out (neighbor-by-neighbor) the munis for this Polly's leading edge location\n",
    "        SNAPPOP = 0.            \n",
    "        for TT in fullPollyTractList:\n",
    "            if DEBUG != \"no\":\n",
    "                plotPoly(TCPLIST[TT].buffer(0.002))\n",
    "            SNAPPOP += TRACTPOP[TT]\n",
    "        if DEBUG != \"no\":\n",
    "            plotPoly(Polly)\n",
    "            plotPoly(TCPLIST[MUNITRACTLIST[HM][0]].buffer(0.01)) #big circle = a tract in the \"home muni\"\n",
    "            plt.show()\n",
    "        if loopNo > maxLoopNo - 0  :  # - 2 or -1 :\n",
    "            for TT in fullPollyTractList:\n",
    "                plotPoly(TCPLIST[TT].buffer(0.002))\n",
    "            plotPoly(Polly)\n",
    "            plt.show()\n",
    "            #print(T,loopNo, SNAPPOP,Polly.area,\"t,muniSnap overall loopNo, SNAPPOP, above Polly area\") \n",
    "        if SNAPPOP < TARGPOP + TOLRPOP:\n",
    "            FPTL, FPML, umSNAPPOP = fullPollyTractList.copy(), fullPollyMuniList.copy(), SNAPPOP #will pass back biggest under-max-pop full muni set\n",
    "        else:\n",
    "            if len(fullPollyMuniList) - len(FPML) == 1:  #we are over target by one muni.  save this muni's number\n",
    "                for MMM in fullPollyMuniList:  #ID this muni that put us too far beyond target\n",
    "                    if MMM not in FPML:\n",
    "                        SPLITMUNI = MMM\n",
    "    return FPTL, FPML, umSNAPPOP, SPLITMUNI\n",
    "\n",
    "def cutVtdSnap(TARGPOP, TOLRPOP, TCPLIST, SUBLIST, NEIGHBORLIST, TRACTPOP,  ANGLE, CUTPOINT,homeV=-999) : \n",
    "    \"\"\"\n",
    "    similar to cutMuniSnap, but here we snap to vtd's, so fewer inputs needed.  Here, the SUBLIST can be a muni's tractlist\n",
    "    we optionally pass in a \"home vtd\" that is forced to be in the collection of vtd's (this ensures we take the right pizza half)\n",
    "    \"\"\"\n",
    "    #first, set a goodsized polygon side length to capture all in tracts on 1 side of a pizza cut\n",
    "    subListTCPs = list()\n",
    "    for V in SUBLIST:\n",
    "        subListTCPs.append(TCPLIST[V])\n",
    "    sliceDiam = 1.5 *getDiag(subListTCPs)  #some buffer vs. a diagonal through the bounding box of all in-play tract centerpoints \n",
    "    origPolly = angledPentaPoly(CUTPOINT, ANGLE,sliceDiam)  \n",
    "    #plotPoly(origPolly)\n",
    "    #define a \"home vtd\" far from the original cutpoint on the correct side of the pizza cut\n",
    "    if homeV == -999: #we need to establish a home vtd; not passed from main\n",
    "        maxVtdDist = 0.\n",
    "        for V in SUBLIST:  #finding the home vtd (= farthest from cut) we are forcing to be included in the vtd-snapped district\n",
    "            #plotPoly(TCPLIST[V].buffer(0.002))\n",
    "            if TCPLIST[V].distance(CUTPOINT) > maxVtdDist and origPolly.contains(TCPLIST[V]):\n",
    "                isConnected = False  #rarely, a semi-surrounded tract was left behind in previous district draw\n",
    "                for vv in NEIGHBORLIST[V]:\n",
    "                    if vv in SUBLIST:\n",
    "                        isConnected = True\n",
    "                if isConnected:\n",
    "                    homeV = V\n",
    "                    maxVtdDist = TCPLIST[V].distance(CUTPOINT)\n",
    "    #plt.show()\n",
    "    guessedCenter = CUTPOINT\n",
    "    CPx = CUTPOINT.x\n",
    "    CPy = CUTPOINT.y\n",
    "    maxLoopNo, fullTolLoopNo = 18, 10\n",
    "    SNAPPOP = 0.\n",
    "    loopNo = 0\n",
    "    dx_dr = -1.*math.sin(ANGLE)  # when moving the cutpoint, we move it perpendicular to the cut angle, hence this reverses sin & cos\n",
    "    dy_dr = math.cos(ANGLE)\n",
    "    SIGN = -1.0   #I get confused about which way we should step the cutpoint.  -1 works with above dx_dr & dy_dr sign convention\n",
    "    abs_dr = 0.5*sliceDiam #initial step size\n",
    "    dr = 0.  #dr is the distance (toward our primary tract) from the original cutpoint to the current guessed center\n",
    "    FPML = list()  #this will become the list of full muni's in the last polygon to capture muni pop's under the target\n",
    "    SPLITVTD = -999 #this will become the vtd we need to split-add to satisfy district Pop (not implemented)\n",
    "    TOL_POP = 0.4*TOLRPOP #aim for narrower range in first few loops if possible\n",
    "    while abs(TARGPOP - SNAPPOP) > TOL_POP and loopNo < maxLoopNo:\n",
    "        if loopNo > fullTolLoopNo:  #loosen target range\n",
    "            TOL_POP = TOLRPOP\n",
    "        #print(\"previous loop, SNAPPOP were\",loopNo, SNAPPOP)\n",
    "        loopNo +=1\n",
    "        sliceList = list()\n",
    "        if loopNo > 1:  #first loop, do not adjust\n",
    "            dr += abs_dr * SIGN*np.sign(TARGPOP - SNAPPOP)\n",
    "            guessedCenter = Point(CPx + dx_dr*dr, CPy + dy_dr*dr)\n",
    "            abs_dr = 0.5 * abs_dr  #stepsize change for next round\n",
    "        Polly = angledPentaPoly(guessedCenter, ANGLE,sliceDiam) \n",
    "        #plotPoly(Polly)\n",
    "        #plotCenter(loopNo, Polly)\n",
    "        #plotPoly(TCPLIST[homeV].buffer(0.05))\n",
    "        #for ttt in SUBLIST:\n",
    "        #    plotPoly(TCPLIST[ttt].buffer(0.02))\n",
    "        #plt.show()\n",
    "        fullPollyTractList = [homeV]\n",
    "        latestPollyTractList = [homeV]\n",
    "        rd = 0\n",
    "        while len(latestPollyTractList) > 0 :  #i.e. while we are still finding neighbors of neighbors in the current Polly\n",
    "            #print(rd,loopNo,\"rd, loop. Last rd found=\",latestPollyMuniList,\"contiguous munis for tract\",t)\n",
    "            rd +=1\n",
    "            previousPollyTractList = latestPollyTractList.copy()\n",
    "            latestPollyTractList = list()\n",
    "            for V in previousPollyTractList:  #all vtd's we captured in last round only\n",
    "                for TT in NEIGHBORLIST[V]:\n",
    "                    if TT not in fullPollyTractList:\n",
    "                        if TT in SUBLIST and Polly.contains(TCPLIST[TT]) : #eligible vtd inside our current polygon\n",
    "                            latestPollyTractList.append(TT)\n",
    "                            fullPollyTractList.append(TT)\n",
    "                                    \n",
    "        # end of while to build out (neighbor-by-neighbor) the munis for this Polly's leading edge location\n",
    "        SNAPPOP = 0.            \n",
    "        for TT in fullPollyTractList:\n",
    "            #plotPoly(TCPLIST[TT].buffer(0.002))\n",
    "            SNAPPOP += TRACTPOP[TT]\n",
    "        #plotPoly(Polly)\n",
    "        #plotPoly(TCPLIST[MUNITRACTLIST[HM][0]].buffer(0.01)) #big circle = a tract in the \"home muni\"\n",
    "        #plt.show()\n",
    "        if loopNo > maxLoopNo - 0  :  # - 2 or -1 :\n",
    "            for TT in fullPollyTractList:\n",
    "                plotPoly(TCPLIST[TT].buffer(0.002))\n",
    "            plotPoly(Polly)\n",
    "            plt.show()\n",
    "            print(loopNo, SNAPPOP,Polly.area,\"vtdSnap overall loopNo, SNAPPOP, above Polly area (did not converge)\") \n",
    "        if SNAPPOP < TARGPOP + TOLRPOP:\n",
    "            FPTL, SNAPPOP = fullPollyTractList, SNAPPOP #will pass back biggest under-max-pop full muni set\n",
    "        #else:\n",
    "        #    if len(fullPollyTractList) - len(FPTL) == 1:  #we are over target by one tract.  save this tract's number\n",
    "        #        for VVV in fullPollyTractList:  #ID this muni that put us too far beyond target\n",
    "        #            if VVV not in FPTL:\n",
    "        #                SPLITVTD = VVV      #plenty of slop in target pop; we never split vtd's like we do muni's\n",
    "        FPTL, SNAPPOP = fullPollyTractList, SNAPPOP  #for vtdsnap (unlike muniSnap), OK to go a little over\n",
    "    return FPTL, SNAPPOP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "44c1e017-a5a4-40c2-a5af-f380295cd4b6",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "def get_straddlers(targetStraddlerPop, COUNTYPOP, COUNTYLIST, INLIST, MUSTINTERSECTGEOM,HDPOLY, TRACTCP, TRACTPOP): #..based on HDPoly\n",
    "    \"\"\"\n",
    "    This function creates a list of \"straddler\"-centering tracts in a county that has at least one whole district plus a partial\n",
    "    the total pop of straddler tracts is an input; this is typically the \"remnant\" pop after forming whole districts in the county\n",
    "    we rank candidate straddlers by their fraction of in-county or ex-county pop in their original HDPoly shapes,\n",
    "    adding straddlers until we reach the target pop; thus biasing straddlers toward near-boundary tracts\n",
    "    the \"INLIST\" is the list of tracts (in-county or selected neighboring counties) that are relevant for the in-HDPoly pop calc\n",
    "    \"\"\"\n",
    "    # for some constrained situations, only HDPoly's that intersect a certain neighboring county can become straddlers. \n",
    "    straddlerType = \"lowInCounty\"\n",
    "    SIGN = 1.\n",
    "    if COUNTYLIST[0] not in INLIST:\n",
    "        straddlerType = \"highExCounty\"\n",
    "        SIGN = -1.\n",
    "    countedPop = [0.]*len(HDPOLY)\n",
    "    if straddlerType == \"lowInCounty\":\n",
    "        countedPop = [COUNTYPOP]*len(HDPOLY)  #inflate these for tracts we don't care about, so they stay high after sorting\n",
    "    for T in COUNTYLIST:\n",
    "        countedPop[T] = 0.  #zero out (again) to cover lowInCounty situation\n",
    "        for tt in INLIST:\n",
    "            if HDPOLY[T].contains(TRACTCP[tt]) and HDPOLY[T].intersects(MUSTINTERSECTGEOM):\n",
    "                countedPop[T] += SIGN * TRACTPOP[tt]  #if we're counting exCounty pop, we want the biggest (most negative) summed pop after sorting\n",
    "    sortedCountedPop = countedPop.copy()\n",
    "    if straddlerType == \"lowInCounty\":\n",
    "        idx = np.argsort(countedPop)\n",
    "        #sortedCountedPop.sort()  #sorting from biggest to smallest inCounty pop   \n",
    "    if straddlerType == \"highExCounty\":\n",
    "        idx = np.argsort(countedPop)\n",
    "        #sortedCountedPop.sort()\n",
    "    \n",
    "    straddlerPop = 0.\n",
    "    halfTractPop = 0.5 * COUNTYPOP / len(COUNTYLIST)  #local average for half of tract population\n",
    "    straddlerTracts = []\n",
    "    counter = 0\n",
    "    while straddlerPop < targetStraddlerPop - halfTractPop:\n",
    "        T = int(idx[counter]) #.index(sortedCountedPop[counter])\n",
    "        straddlerPop += TRACTPOP[T]\n",
    "        straddlerTracts.append(T)\n",
    "        counter +=1\n",
    "    return straddlerTracts\n",
    "# ***********************\n",
    "def get_ppCstraddlers(targetStraddlerPop, COUNTYPOP, COUNTYLIST, mCstraddlerList, HDTRACTLIST, TRACTPOP) : #similar to above, but via tract list, not HDPoly\n",
    "    #straddlerType = \"highExCounty\"  #ppC's always use the highExCounty logic driven by the mC's straddler tractlist\n",
    "    SIGN = -1.                       #... so we use negative sign\n",
    "    countedPop = [0.]*len(TRACTPOP)\n",
    "    for T in COUNTYLIST:\n",
    "        for tt in mCstraddlerList:\n",
    "            if T in HDTRACTLIST[tt] :\n",
    "                countedPop[T] += SIGN * TRACTPOP[tt]  #if we're counting exCounty pop, we want the biggest (most negative) summed pop after sorting\n",
    "    #sortedCountedPop = countedPop.copy()  \n",
    "    idx = np.argsort(countedPop)\n",
    "    #if straddlerType = \"highExCounty\":\n",
    "    #sortedCountedPop.sort()\n",
    "    \n",
    "    straddlerPop = 0.\n",
    "    halfTractPop = 0.5 * COUNTYPOP / len(COUNTYLIST) \n",
    "    straddlerTracts = []\n",
    "    counter = 0\n",
    "    while straddlerPop < targetStraddlerPop - halfTractPop:\n",
    "        T = int(idx[counter])  #.index(sortedCountedPop[counter])\n",
    "        straddlerPop += TRACTPOP[T]\n",
    "        straddlerTracts.append(T)\n",
    "        counter +=1\n",
    "    return straddlerTracts\n",
    "# *******************************\n",
    "\n",
    "def getSliceAngle(pointA, CUTPOINT, pointC):\n",
    "    \"\"\"\n",
    "    This method sets the pizza cutting angle to be PERPENDICULAR to a line from pointA to the cutPoint (B), oriented to include pointC \n",
    "    pointA is often the center of the Home District or close-to-HDCP pt, point B is the nominal center of a partial county to be added,\n",
    "     and pointC is a point in the partial county that's close to the Home District center, so should be included.\n",
    "    \"\"\"\n",
    "    dx,dy = CUTPOINT.x - pointA.x, CUTPOINT.y - pointA.y\n",
    "    SLICEANGLE = 0.  #horizontal cut if pointA and CUTPOINT share an x coordinate\n",
    "    if dx != 0.:\n",
    "        SLICEANGLE = math.atan(dy/dx) + 0.5*math.pi  #remember, PERPENDICULAR so we add 90 degrees\n",
    "    \n",
    "    Polly = angledPentaPoly(CUTPOINT, SLICEANGLE, 5.*pointC.distance(CUTPOINT)) #a large shape that includes correct half of pizza\n",
    "    #plotPoly(pointA.buffer(0.1))  #debug plotting ...\n",
    "    #plotPoly(CUTPOINT.buffer(0.2))\n",
    "    #plotPoly(pointC.buffer(0.3))\n",
    "    #plotPoly(pointC.buffer(0.01))\n",
    "    if Polly.disjoint(pointC):   #oops, we need the complementary math.atan solution to capture the target pointC\n",
    "        SLICEANGLE += math.pi\n",
    "        Polly = angledPentaPoly(CUTPOINT, SLICEANGLE, pointC.distance(CUTPOINT))\n",
    "    #plotPoly(Polly)\n",
    "    return SLICEANGLE\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3f2980a0-2b23-4e26-aec4-a454d412608e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def getFarSheddableMuni(cT_CP, POPGLUT, aDP, MUNIPOP, MUNIPCP, MOPUPMUNILIST):   #I don't think this f'n is used\n",
    "    \"\"\"\n",
    "    Find the farthest qualifying muni from the 'closest tract' to the HD center that is large enough such that if split,\n",
    "    can shed enough pop to get us within tolerance for district pop\n",
    "    Muni's with 0.5 d < muniPop < 1.0 d are disqualified from this list\n",
    "    \"\"\"\n",
    "    splitCandidates = list()\n",
    "    splitCdistance = list()\n",
    "    for m in MOPUPMUNILIST:\n",
    "        if MUNIPOP[m] < 0.5 * aDP and MUNIPOP[m] > POPGLUT:  #remember, we already split all big muni's\n",
    "            splitCandidates.append(m)\n",
    "            splitCdistance.append(MUNIPCP[m].distance(cT_CP) )\n",
    "    if len(splitCandidates) > 0:\n",
    "        ourSplitMuni = splitCandidates[splitCdistance.index(np.max(splitCdistance))]\n",
    "    else:\n",
    "        ourSplitMuni = -999\n",
    "    return ourSplitMuni \n",
    "\n",
    "def getNearSplitMuni(cT_CP, POPGAP, aDP, MUNIPOP, MUNIPCP, MUNINEIGHBORLIST,INHD_MUNILIST, CANDIDATELIST): #I don't think this f'n is used\n",
    "    \"\"\"\n",
    "    ?? not used?  getFillList3 may supersede .... ?\n",
    "    The CANDIDATELIST is a list of muni's in the target county that are not yet incorporated into the HD\n",
    "    Find the closest qualifying muni to the 'closest tract' to the HD center.\n",
    "    To qualify, must be splittable and can contribute enough pop to get us within tolerance for district pop\n",
    "    Muni's with 0.5 d < muniPop < 1.0 d are disqualified from this list, as are muni's not continuous with at least one whole muni in the HD\n",
    "    Note: this code will fail if an unsplittable muni is our only option (e.g. Lorain). \n",
    "       In that case, we'll use another snippet to grow around the unsplittable muni\n",
    "    \"\"\"\n",
    "    splitCandidates = list()\n",
    "    splitCdistance = list()\n",
    "    for m in CANDIDATELIST:\n",
    "        if MUNIPOP[m] < 0.5 * aDP and MUNIPOP[m] > POPGAP:  #remember, we already split all big muni's\n",
    "            isAdjacent = \"no\"\n",
    "            for mm in INHD_MUNILIST:\n",
    "                if m in MUNINEIGHBORLIST[mm]:\n",
    "                    isAdjacent = \"yes\"\n",
    "                    splitCandidates.append(m)\n",
    "                    splitCdistance.append(MUNIPCP[m].distance(cT_CP) )\n",
    "                    break\n",
    "    if len(splitCandidates) > 0:\n",
    "        nearSplitMuni = splitCandidates[splitCdistance.index(np.min(splitCdistance))]\n",
    "    else:\n",
    "        nearSplitMuni = -999\n",
    "    return nearSplitMuni \n",
    "\n",
    "def getFillList3(CT, TGTPOPf, TOLERf, ADP, MUNIPOP, MUNIPCP, MUNITRACTLIST, MUNINEIGHBORLIST,INHD_MUNILIST, C_MUNILIST,\n",
    "                TCPLIST, NEIGHBORLIST, TRACTPOP):\n",
    "    \"\"\"\n",
    "    This code completes a district via whole munis and 1 partial when the logical muni to complete a district cannot be split\n",
    "    The CANDIDATELIST is a list of qualifying muni's in the target county that are not yet incorporated into the HD\n",
    "    \"Qualifying\" munis are splittable and adjacent to muni's already in the HD. (we update this list as we go)\n",
    "    The INHD_MUNILIST lists all muni's already in the HD. (the popGap was already diminished by these added muni's to aDP)\n",
    "    We build out from closest to the \"closestTract_CenterPoint\" by full muni's until we pick a splittable muni that completes the HD\n",
    "     or adding whole muni's gets us to the target range.  We do not bias toward not splitting; this was already tried in another f'n\n",
    "    \"\"\"\n",
    "    CANDIDATELIST = C_MUNILIST.copy()\n",
    "    for m in CANDIDATELIST.copy():  #this could be imported as ALL of a county's muni's, so need to disqualify some ...\n",
    "        if m in INHD_MUNILIST or MUNIPOP[m] > 0.5*ADP:  #don't worry that this rejects all midsized muni's; they wouldn't fit anyway ...\n",
    "            CANDIDATELIST.remove(m)                    #... b/c we call this code only after we've added the closest whole muni's\n",
    "    addedPop = 0.\n",
    "    fillLIST3 = list()\n",
    "    addedMunis = INHD_MUNILIST.copy()\n",
    "    #print(\"candidate list, inHDalready muni lists are\",CANDIDATELIST, addedMunis)\n",
    "    while addedPop < TGTPOPf - TOLERf:  #each round, we look for neighbors of inHD muni's\n",
    "        nList = list()   #resetting the list of remaining available neighbor munis and their distances\n",
    "        nDistance = list()\n",
    "        if addedMunis == list():  #(Rare) Natural first muni to add from this county was too big to fit in. Below could reselect it.\n",
    "            #print(\"no muni's already in HD from this list.  Pick closest candidate\")\n",
    "            for m in CANDIDATELIST:\n",
    "                nList.append(m)\n",
    "                nDistance.append(TCPLIST[CT].distance(MUNIPCP[m]))\n",
    "        else: #we do have at least one muni already in the HD county remnant.  Only muni's contiguous to this list are in play\n",
    "            for mm in addedMunis:\n",
    "                for m in MUNINEIGHBORLIST[mm]:\n",
    "                    if m in CANDIDATELIST and m not in addedMunis and m not in nList:\n",
    "                        nList.append(m)\n",
    "                        nDistance.append(TCPLIST[CT].distance(MUNIPCP[m]) )\n",
    "        newMuni = nList[nDistance.index(np.min(nDistance))]  #closest contiguous candidate gets fully or partially added\n",
    "        addedMunis.append(newMuni)\n",
    "        if MUNIPOP[newMuni] > (TGTPOPf + TOLERf - addedPop):  #can't fit the whole muni in, so we find the partial to complete the HD\n",
    "            CUTPOINT = MUNIPCP[newMuni]\n",
    "            contiguousVTDs = list()\n",
    "            for MM in MUNINEIGHBORLIST[newMuni]:  #remember, muni's are not self-neighbors               \n",
    "                if MM in addedMunis:  #ensuring that at least one vtd in selected split muni is contiguous to addedMunis\n",
    "                    for VV in MUNITRACTLIST[MM]:\n",
    "                        for V in MUNITRACTLIST[newMuni]:\n",
    "                            if V in NEIGHBORLIST[VV] and V not in contiguousVTDs:\n",
    "                                contiguousVTDs.append(V)\n",
    "            includedVTD = getClosestTract(TCPLIST[CT], contiguousVTDs, TCPLIST) \n",
    "            ANGLE = getSliceAngle(TCPLIST[CT], MUNIPCP[newMuni],TCPLIST[includedVTD])\n",
    "            SUBLIST = MUNITRACTLIST[newMuni]\n",
    "            addedVTDlist, newPop = cutVtdSnap(TGTPOPf, TOLERf, TCPLIST, SUBLIST, NEIGHBORLIST, TRACTPOP, ANGLE, CUTPOINT)\n",
    "            addedPop += newPop\n",
    "            #print(\"added\", newPop,\"now our total getFillList3 pop is\",addedPop)\n",
    "            fillLIST3 = fillLIST3 + addedVTDlist\n",
    "            break\n",
    "        else: #we CAN fit the whole muni\n",
    "            addedPop += MUNIPOP[newMuni]\n",
    "            fillLIST3 = fillLIST3 + MUNITRACTLIST[newMuni]\n",
    "            #print(\"added a full muni, our total getFillList3 pop is\", addedPop)\n",
    "            \n",
    "    fillPOP = 0\n",
    "    for TT in fillLIST3:\n",
    "        fillPOP += TRACTPOP[TT]  #should = addedPop, but recalc just in case\n",
    "    return fillLIST3 , fillPOP #these are the vtd's we will add to the previous list of full muni's in the muni-snapped HD\n",
    "\n",
    "def getFillList3noSplit(CT, TGTPOPn, TOLERn, ADP, MUNIPOP, MUNIPCP, MUNITRACTLIST, MUNINEIGHBORLIST,INHD_MUNILIST, C_MUNILIST,\n",
    "                TCPLIST, NEIGHBORLIST, TRACTPOP):\n",
    "    \"\"\"\n",
    "    This code completes a district via WHOLE munis NO PARTIALS when the logical muni to complete a district cannot be split\n",
    "    The CANDIDATELIST is a list of qualifying muni's in the target county that are not yet incorporated into the HD\n",
    "    \"Qualifying\" munis are adjacent to muni's already in the HD (we update this list as we go)\n",
    "    The INHD_MUNILIST lists all muni's already in the HD. (the popGap was already diminished by these added muni's to aDP)\n",
    "    We build out from closest to the \"closestTract_CenterPoint\" by full muni's until we reach the target range\n",
    "    \"\"\"\n",
    "    CANDIDATELIST = C_MUNILIST.copy()\n",
    "    for m in CANDIDATELIST.copy():  #this could be imported as ALL of a county's muni's, so need to disqualify some ...\n",
    "        if m in INHD_MUNILIST: # or MUNIPOP[m] > 0.5*ADP:  #not needed; large munis won't fit anyway in below code\n",
    "            CANDIDATELIST.remove(m)\n",
    "    addedPop = 0.\n",
    "    FILL_LIST3 = list()\n",
    "    addedMunis = INHD_MUNILIST.copy()\n",
    "    bannedMuniList = list()  #muni's that are too big to add whole\n",
    "    #print(\"prior to gFL3nS loops, addedPop, TGTPOP, TOLER are\",addedPop, TGTPOP, TOLER)\n",
    "    while addedPop < TGTPOPn - TOLERn:  #each round, we look for neighbors of inHD muni's\n",
    "        nList, nDistance = list(), list()\n",
    "        for mm in addedMunis:\n",
    "            for m in MUNINEIGHBORLIST[mm]:\n",
    "                if m in CANDIDATELIST and m not in addedMunis and m not in nList and m not in bannedMuniList:\n",
    "                    if MUNIPOP[m] > TGTPOPn + TOLERn - addedPop:\n",
    "                        bannedMuniList.append(m)  #these munis will put us over the limit, so don't consider them\n",
    "                    else:\n",
    "                        nList.append(m)\n",
    "                        nDistance.append(TCPLIST[CT].distance(MUNIPCP[m]) )\n",
    "        #print(\"added,nList, nDistances\",addedMunis,nList,nDistance)\n",
    "        if nList == list() : #there are no adjacent muni's small enough to fit in, or we had no full muni's coming into this.  Punt!!\n",
    "            FILL_LIST3 = []  #our flag that we failed\n",
    "            break\n",
    "        else:\n",
    "            newMuni = nList[nDistance.index(np.min(nDistance))]  #closest contiguous candidate gets fully added\n",
    "            addedMunis.append(newMuni)\n",
    "            addedPop += MUNIPOP[newMuni]\n",
    "            FILL_LIST3 = FILL_LIST3 + MUNITRACTLIST[newMuni]\n",
    "            \n",
    "    FILLPOP = 0\n",
    "    for TT in FILL_LIST3:\n",
    "        FILLPOP += TRACTPOP[TT]\n",
    "    return FILL_LIST3 , FILLPOP #these are the vtd's we will add to the previous list of full muni's in the muni-snapped HD\n",
    "\n",
    "def tryNoSplit(hT, TGTPOPt, TOLERt,ADP, MUNIPOP, MUNIPCP, MUNITRACTLIST, MUNINEIGHBORLIST, INHD_MUNILIST, C_MUNILIST,\n",
    "               TRACTCP, NEIGHBORLIST, TRACTPOP3, NSPLITS) :\n",
    "    \"\"\"\n",
    "    This code is triggered when building a pairing county's in-district population out of whole muni's undershoots the target range,\n",
    "    because the natural muni to complete the district is too big to add as whole\n",
    "    Our first try is getFillList3noSplit to find other (smaller) whole muni's to fill the population gap\n",
    "      If that fails, if we have not yet created a split in this district (e.g. in other counties), we leverage a split,\n",
    "        otherwise we give up because a district can't have two splits\n",
    "    The C_MUNILIST is a list of candidate muni's (e.g. all in a county) that could be added, including those already INHD_MUNILIST\n",
    "    \"\"\"\n",
    "    VLIST3, VPOP3 = getFillList3noSplit(hT, TGTPOPt, TOLERt, ADP, MUNIPOP, MUNIPCP, MUNITRACTLIST, MUNINEIGHBORLIST, INHD_MUNILIST,\n",
    "                                                    C_MUNILIST,TRACTCP, NEIGHBORLIST, TRACTPOP3)\n",
    "    #print(\"gFL3nS pop (vs tgt), list =\",vPop3,\"(\",TGTPOP,\")\",vList3)\n",
    "    if VPOP3 == 0: #gFL3nS failed\n",
    "        NSPLITS +=1\n",
    "        #print(\"adding a split, total nSplits =\",nSplits)\n",
    "        if NSPLITS <= 1: #allow a split to hit target pop.  We know we will generate a split if above noSplit call failed\n",
    "            VLIST3, VPOP3 = getFillList3(hT, TGTPOPt, TOLERt, ADP, MUNIPOP, MUNIPCP, MUNITRACTLIST, MUNINEIGHBORLIST, INHD_MUNILIST,\n",
    "                                         C_MUNILIST, TRACTCP, NEIGHBORLIST, TRACTPOP3)\n",
    "    return VLIST3, VPOP3, NSPLITS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "806b474e-9523-422f-b40b-70a7f1fc2f4e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# for snapping to whole muni's close to original HD shape ...  2/26/23 modified\n",
    "def buildPolly(CENTERPOINT, ANGLE, RADIUS):\n",
    "    hdCPx = CENTERPOINT.x\n",
    "    hdCPy = CENTERPOINT.y\n",
    "    Pt = [Point(0,0)]*8\n",
    "    for nW in range(4):\n",
    "        ccwAngle = ANGLE[nW]  #these two are the angles at start and end of nth wedge\n",
    "        cwAngle = ANGLE[ int((nW+1)%4) ]\n",
    "        Pt[nW*2] =   Point(hdCPx + RADIUS[nW]*math.cos(ccwAngle),\n",
    "                           hdCPy + RADIUS[nW]*math.sin(ccwAngle) )\n",
    "        Pt[nW*2+1] = Point(hdCPx + RADIUS[nW]*math.cos(cwAngle),\n",
    "                           hdCPy + RADIUS[nW]*math.sin(cwAngle) )        \n",
    "    POLLY = Polygon([Pt[0],Pt[1],Pt[2],Pt[3],Pt[4],Pt[5],Pt[6],Pt[7] ])\n",
    "    return POLLY\n",
    "\n",
    "def HDvtdSnap(T, TARGPOP, TOLRPOP, TCPLIST, ALLOWEDLIST, NEIGHBORLIST, TRACTPOP, ANGLE, ORIG_R, inHD_V=-999) : \n",
    "    \"\"\"\n",
    "    This is like cutVtdSnap, but the shape of the original HD guides our pick-up of vtd's to meet the target, vs a \"pizza cut\"\n",
    "    Each loop, we guess an expansion or shrinkage of HD Poly, see how many contiguous vtd's we captured vs. target\n",
    "    User must pass the centering tract T and a \"inHD_V\" vtd (close tract) that must be in the final list\n",
    "    The ALLOWEDLIST is the list of vtd's we can pull from (e.g. a particular countyTractList)\n",
    "    The NEIGHBORLIST is each vtd's list of neighbors.  ANGLE and ORIG_R define the original (wedge-based) HD Polygon\n",
    "    \"\"\"\n",
    "    \n",
    "    if inHD_V == -999: #we need to establish a starter vtd; not passed from main\n",
    "        origPolly = buildPolly(TCPLIST[T], ANGLE, ORIG_R)\n",
    "        minVtdDist = 88888.  #big number\n",
    "        for V in ALLOWEDLIST:  #finding the home vtd (= farthest from cut) we are forcing to be included in the vtd-snapped district\n",
    "            if TCPLIST[V].distance(TCPLIST[T]) < minVtdDist and origPolly.contains(TCPLIST[V]):\n",
    "                inHD_V = V\n",
    "                minVtdDist = TCPLIST[V].distance(TCPLIST[T])\n",
    "    maxLoopNo,fullTolLoopNo = 23, 10\n",
    "    minPrintLoopNo = maxLoopNo - 4 #1\n",
    "    minBreakLoopNo = maxLoopNo - 5  #should be near equilibrium by this point\n",
    "    SNAPPOP = 0.\n",
    "    Rratio = 0.001  #this will make first Rratio = 1.001\n",
    "    dr = 1.0\n",
    "    loopNo = 0\n",
    "    fullBreak = False  #trigger to end early if a round puts us over the target pop\n",
    "    TOL_POP = 0.4*TOLRPOP\n",
    "    while abs(TARGPOP - SNAPPOP) > TOL_POP and loopNo < maxLoopNo:\n",
    "        if loopNo > fullTolLoopNo:  #aim for narrower range in first few loops if possible\n",
    "            TOL_POP = TOLRPOP\n",
    "        loopNo += 1\n",
    "        Rratio = Rratio + dr * np.sign(TARGPOP - SNAPPOP)\n",
    "        dr = dr * 0.5   #bisection for next round\n",
    "        CURRENTR = [Rratio*ORIG_R[nW] for nW in range(4)]  #same as cR, but passed to subloops       \n",
    "        Polly = buildPolly(TCPLIST[T], ANGLE, CURRENTR)\n",
    "        SNAPPOP = 0.\n",
    "        latestPollyTractList, fullPollyTractList = [inHD_V], [inHD_V]   #starter vtd\n",
    "        SNAPPOP += TRACTPOP[inHD_V]\n",
    "        round = 0\n",
    "        while len(latestPollyTractList) > 0 :  #i.e. while we are still finding neighbors of neighbors in the current Polly\n",
    "            round +=1\n",
    "            previousPollyTractList = latestPollyTractList.copy()\n",
    "            latestPollyTractList = list()\n",
    "            nlist = list()\n",
    "            for V in previousPollyTractList: \n",
    "                for TT in NEIGHBORLIST[V]:\n",
    "                    if TT not in fullPollyTractList and TT in ALLOWEDLIST and TT not in nlist:\n",
    "                        nlist.append(TT)  #so we don't examine it again\n",
    "                        if Polly.contains(TCPLIST[TT]) :\n",
    "                            if SNAPPOP > TARGPOP and loopNo >= minBreakLoopNo:\n",
    "                                stopAddingVTDs = True\n",
    "                            else:\n",
    "                                SNAPPOP += TRACTPOP[TT]\n",
    "                                latestPollyTractList.append(TT)\n",
    "                                fullPollyTractList.append(TT)\n",
    "        # end of while to build out (neighbor-by-neighbor) the munis and bigMuni tracts for this Polly's radius ratio\n",
    "        if loopNo >= minPrintLoopNo: #debugging\n",
    "            print(\"T, loop,Rratio,SNAPPOP,nTracts included\",T,loopNo,Rratio,SNAPPOP,len(fullPollyTractList))\n",
    "\n",
    "    return fullPollyTractList, SNAPPOP\n",
    "\n",
    "def HDmuniSnap(T,TARGPOP, TOLRPOP, MUNITRACTLIST,TCPLIST, ALLOWEDLIST, NEIGHBORLIST, TRACTPOP, MUNIPOP, MUNIPCP, ANGLE, ORIG_R,\n",
    "               inHD_M = -999) : \n",
    "    \"\"\"\n",
    "    This is like cutMuniSnap, but the shape of the original HD guides our pick-up of whole muni's to meet the target,\n",
    "    so the logic aligns with HDvtdSnap, but for muni's\n",
    "    Each loop, we guess an expansion or shrinkage of HD Poly, see how many contiguous muni's we captured vs. target\n",
    "    # vs std muniSnap, \"muniBreakSnap\" will terminate as soon as targPop met, if the loopNo > minBreakLoopNo\n",
    "    #modified muniBreakSnap from OH_Congr_multisnap / OH_patch_legisl_org, but with no big munis\n",
    "    User must pass the centering tract T, but we figure out the home muni from the muniTractLists\n",
    "    The ALLOWEDLIST is the list of muni's we can pull from (e.g. a particular countyMuniList)\n",
    "    \"\"\"\n",
    "    if inHD_M == -999:  #\"starter\" / home muni forced to be in the collection has not been specified. Find a close one to HDCP\n",
    "        minMuniDist = 888888.\n",
    "        #print(len(ALLOWEDLIST),\"is the length of our allowed list\")\n",
    "        for M in ALLOWEDLIST: #closest muni in the allowed list will be set as home muni\n",
    "            if MUNIPCP[M].distance(TCPLIST[T]) < minMuniDist:\n",
    "                HM = M\n",
    "                minMuniDist = MUNIPCP[M].distance(TCPLIST[T])\n",
    "    else:\n",
    "        HM = inHD_M\n",
    "    #print(T,\"the HDmuniSnap home muni no=\",HM,\"with pop\",MUNIPOP[HM],\"CP=\",r3(MUNIPCP[HM].x),r3(MUNIPCP[HM].y))\n",
    "    maxLoopNo,fullTolLoopNo = 23, 10\n",
    "    minPrintLoopNo = maxLoopNo - 1 #1\n",
    "    minBreakLoopNo = maxLoopNo - 5  #should be near equilibrium by this point\n",
    "    SNAPPOP, notTooBigSnapPop = 0., 0.\n",
    "    FPML, FPTL = list(),list()  #this will become the list of full muni's and vtd's in the last polygon to capture muni pop's under the target\n",
    "    Rratio = 0.001  #this will make first Rratio = 1.001\n",
    "    dr = 1.0\n",
    "    loopNo = 0\n",
    "    fullBreak = False  #trigger to end early if a round puts us over the target pop\n",
    "    TOL_POP = 0.4*TOLRPOP\n",
    "    while abs(TARGPOP - SNAPPOP) > TOL_POP and loopNo < maxLoopNo:\n",
    "        if loopNo > fullTolLoopNo:  #aim for narrower range in first few loops if possible\n",
    "            TOL_POP = TOLRPOP\n",
    "        loopNo += 1\n",
    "        Rratio = Rratio + dr * np.sign(TARGPOP - SNAPPOP)\n",
    "        dr = dr * 0.5   #bisection for next round\n",
    "        CURRENTR = [Rratio*ORIG_R[nW] for nW in range(4)]  #same as cR, but passed to subloops       \n",
    "        Polly = buildPolly(TCPLIST[T], ANGLE, CURRENTR)\n",
    "        SNAPPOP = 0.\n",
    "        fullPollyTractList = list()\n",
    "        latestPollyMuniList = [HM]\n",
    "        fullPollyMuniList = [HM]           \n",
    "        fullPollyTractList = MUNITRACTLIST[HM].copy()   #HD absorbs full home muni\n",
    "        SNAPPOP += MUNIPOP[HM]\n",
    "        round = 0\n",
    "        while len(latestPollyMuniList) > 0 :  #i.e. while we are still finding neighbors of neighbors in the current Polly\n",
    "            round +=1\n",
    "            #print(loopNo,round,\"loop,rd. Last rd found=\",latestPollyMuniList,\"contiguous munis for tract\",t,\"snappedPop was\",SNAPPOP)\n",
    "            previousPollyMuniList = latestPollyMuniList.copy()\n",
    "            latestPollyMuniList = list()\n",
    "            nlist = list()\n",
    "            for M in previousPollyMuniList: \n",
    "                for MM in NEIGHBORLIST[M]:\n",
    "                    if MM not in fullPollyMuniList and MM in ALLOWEDLIST and MM not in nlist:\n",
    "                        nlist.append(MM)  #so we don't pick it again\n",
    "                        capturedPop = 0.\n",
    "                        for TT in MUNITRACTLIST[MM]:\n",
    "                            if Polly.contains(TCPLIST[TT]) :\n",
    "                                capturedPop += TRACTPOP[TT]\n",
    "                        if capturedPop > 0.5 * MUNIPOP[MM]:  #muni capture threshold\n",
    "                            latestPollyMuniList.append(MM)\n",
    "                            fullPollyMuniList.append(MM)\n",
    "                            fullPollyTractList = fullPollyTractList + MUNITRACTLIST[MM]\n",
    "                            SNAPPOP += MUNIPOP[MM]\n",
    "\n",
    "                        if SNAPPOP > TARGPOP and loopNo >= minBreakLoopNo:\n",
    "                            fullBreak = True  #stop\n",
    "                        if fullBreak:  #don't look at any more neighbors of this budding muni\n",
    "                            break\n",
    "                    if fullBreak: #for safety.  I don't think this ever triggers\n",
    "                        break\n",
    "                if fullBreak:  #don't explore any other budding muni's\n",
    "                    break\n",
    "            if fullBreak:  #no more rounds\n",
    "                break\n",
    "        if fullBreak:  #no more changes in Polly radius\n",
    "            break\n",
    "        # end of while to build out (neighbor-by-neighbor) the munis and bigMuni tracts for this Polly's radius ratio\n",
    "        SNAPPOP = 0.            \n",
    "        for TT in fullPollyTractList:\n",
    "            #plotPoly(TCPLIST[TT].buffer(0.002))\n",
    "            SNAPPOP += TRACTPOP[TT]\n",
    "        if loopNo >= minPrintLoopNo:\n",
    "            print(\"loop,Rratio,SNAPPOP,nMuni\",loopNo,Rratio,SNAPPOP,len(fullPollyMuniList))\n",
    "        #plotPoly(Polly)\n",
    "        #plotPoly(muniGeom[1380])\n",
    "        #plt.show()\n",
    "        SPLITMUNI = -999\n",
    "        if SNAPPOP < TARGPOP + TOLRPOP:\n",
    "            FPTL, FPML, notTooBigSnapPop = fullPollyTractList, fullPollyMuniList, SNAPPOP #will pass back biggest under-max-pop full muni set\n",
    "        else:\n",
    "            if len(fullPollyMuniList) - len(FPML) == 1:  #we are over target by one muni.  We want to save this muni's number\n",
    "                for MMM in fullPollyMuniList:  #ID this muni that put us too far beyond target = leading splitMuni candidate\n",
    "                    if MMM not in FPML:\n",
    "                        SPLITMUNI = MMM\n",
    "    return FPTL, FPML, notTooBigSnapPop, SPLITMUNI\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "661e5f3d-e857-4f59-a5f7-f35b68bf0d1a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#adding 3/26/23 - growing district (pieces) from a starting point\n",
    "def vtdSwell(CLOSEPT, TARGPOP, TOLRPOP, TCPLIST, SUBLIST, NEIGHBORLIST, TRACTPOP, xyRatio = 1.0):\n",
    "    \"\"\"\n",
    "    This swells a district of pop TARGPOP +/- TOLRPOP from a SUBLIST, that has global locations, pops of TCPLIST, TRACTPOP\n",
    "    At each step, of the vtd's contiguous to the growing list, we add the closest to the CLOSEPT\n",
    "    the xyRatio can be modified from 1.0 to skew toward wide or tall district shapes\n",
    "    This code often useful when growing partials near a county boundary, so similar to GetFill\n",
    "    'TCPLIST', NEIGHBORLIST', 'TRACTPOP' are the global list of tract centerpoints, neighbors and pops.\n",
    "    'SUBLIST' is the list of vtd's we can draw from in growing the district\n",
    "    \"\"\"\n",
    "    vtd0 = getClosestTract(CLOSEPT, SUBLIST, TCPLIST)\n",
    "    swellList, swellPop = [vtd0], TRACTPOP[vtd0]\n",
    "    nDist = [xyRatio* (TCPLIST[SUBLIST[i]].x - CLOSEPT.x)**2 + (TCPLIST[SUBLIST[i]].y - CLOSEPT.y)**2 for i,s in enumerate(SUBLIST)]\n",
    "    \n",
    "    while swellPop < TARGPOP - TOLRPOP: #each round\n",
    "        nList = list()\n",
    "        nListDist = list()\n",
    "        for V in swellList:\n",
    "            for VV in NEIGHBORLIST[V]:\n",
    "                if VV in SUBLIST and VV not in nList and VV not in swellList and TRACTPOP[VV] + swellPop < TARGPOP + TOLRPOP: #can fit in\n",
    "                    nList.append(VV)\n",
    "                    nListDist.append(nDist[SUBLIST.index(VV)] )\n",
    "        newV = nList[nListDist.index(np.min(nListDist))]\n",
    "        swellList.append(newV)\n",
    "        swellPop += TRACTPOP[newV]\n",
    "        \n",
    "    return swellList, swellPop        \n",
    "                    \n",
    "\n",
    "def muniSwell(CLOSEPT, TARGPOP, TOLRPOP ,ADP, MUNIPOP, MUNIPCP, MUNITRACTLIST,  MUNINEIGHBORLIST, MUNIcLIST,\n",
    "               TRACTCP, NEIGHBORLIST, TRACTPOP, NSPLITS, xyRatio = 1.0):\n",
    "    \"\"\"\n",
    "    similar logic for vtdSwell, but we add by muni, not by vtd. \n",
    "    If we can't add a full muni: if district not yet split, add a split muni a la getFillList, otherwise stop short\n",
    "    The MUNIcLIST is usually a county's list of muni's, but it could be a different curated list\n",
    "    \"\"\"\n",
    "    \n",
    "    newNsplits = NSPLITS #how many splits in the district of which this is a partial piece\n",
    "    muni0 = getClosestTract(CLOSEPT, MUNIcLIST, MUNIPCP)  #really a \"getClosestMuni\" call, but same logic\n",
    "    if MUNIPOP[muni0] > TARGPOP + TOLRPOP :  #can't add the first full muni, so do a vtdSwell within this muni's vtd's\n",
    "        swellList, swellPop = vtdSwell(CLOSEPT, TARGPOP, TOLRPOP, TRACTCP, MUNITRACTLIST[muni0], NEIGHBORLIST, TRACTPOP, xyRatio)\n",
    "        newNsplits += 1\n",
    "    else:  #fully add the closest muni and build from there        \n",
    "        muniList, swellList, swellPop = [muni0], MUNITRACTLIST[muni0], MUNIPOP[muni0]\n",
    "        nDist = [xyRatio* (MUNIPCP[MUNIcLIST[i]].x - CLOSEPT.x)**2 + (MUNIPCP[MUNIcLIST[i]].y - CLOSEPT.y)**2 for i,s in enumerate(MUNIcLIST)]   \n",
    "        canAdd = True\n",
    "        while swellPop < TARGPOP - TOLRPOP and canAdd: #each round, add the closest addable muni\n",
    "            nList = list()\n",
    "            nListDist = list()\n",
    "            for M in muniList:\n",
    "                for MM in MUNINEIGHBORLIST[M]:\n",
    "                    if MM in MUNIcLIST and MM not in nList and MM not in muniList and MUNIPOP[MM] + swellPop < TARGPOP + TOLRPOP: #can fit in\n",
    "                        nList.append(MM)\n",
    "                        nListDist.append(nDist[MUNIcLIST.index(MM)] )\n",
    "            if len(nList) > 0:\n",
    "                newM = nList[nListDist.index(np.min(nListDist))]\n",
    "                muniList.append(newM)\n",
    "                swellList = swellList + MUNITRACTLIST[newM]\n",
    "                swellPop += MUNIPOP[newM]\n",
    "            else:\n",
    "                canAdd = False\n",
    "\n",
    "        if canAdd == False and nSplits < 1:  #got stuck; add a partial muni from splittable in-county neighbors of muni's already in\n",
    "            nList, nListDist, muniListNeighbors = list(), list(), list()\n",
    "            for M in muniList:\n",
    "                muniListNeighbors += MUNINEIGHBORLIST[M]  #OK that this creates duplicates\n",
    "            for MM in MUNIcLIST:\n",
    "                if MM in muniListNeighbors and MM not in muniList and MUNIPOP[MM] < 0.5 * ADP :\n",
    "                    nList.append(MM)\n",
    "                    nListDist.append(nDist[MUNIcLIST.index(MM)] )\n",
    "            splitMuni = nList[nListDist.index(np.min(nListDist)) ]\n",
    "            POPGAP = TARGPOP - swellPop\n",
    "            contiguousCandidates = list()  #the vtds in the splitMuni that are contiguous to the partially-built district\n",
    "            for V in MUNITRACTLIST[splitMuni]:\n",
    "                for VV in swellList:\n",
    "                    if V in NEIGHBORLIST[VV] and V not in contiguousCandidates :\n",
    "                        contiguousCandidates.append(V)\n",
    "                        break\n",
    "            CLOSEPT2 = TRACTCP[ getClosestTract(CLOSEPT, contiguousCandidates, TRACTCP)]\n",
    "            splitList, sPop = vtdSwell(CLOSEPT2, POPGAP, TOLRPOP, TRACTCP, MUNITRACTLIST[splitMuni], NEIGHBORLIST, TRACTPOP, xyRatio)\n",
    "            swellList = swellList + splitList\n",
    "            if len(splitList) > 0:\n",
    "                newNsplits +=1\n",
    "            for V in splitList:\n",
    "                swellPop += TRACTPOP[V]\n",
    "                \n",
    "    return swellList, swellPop, newNsplits\n",
    "                                 \n",
    "                                 \n",
    "def getFillList3(CT, TGTPOPf, TOLERf, ADP, MUNIPOP, MUNIPCP, MUNITRACTLIST, MUNINEIGHBORLIST,INHD_MUNILIST, C_MUNILIST,\n",
    "                TCPLIST, NEIGHBORLIST, TRACTPOP):\n",
    "    \"\"\"\n",
    "    This code completes a district via whole munis and 1 partial when the logical muni to complete a district cannot be split\n",
    "    The CANDIDATELIST is a list of qualifying muni's in the target county that are not yet incorporated into the HD\n",
    "    \"Qualifying\" munis are splittable and adjacent to muni's already in the HD. (we update this list as we go)\n",
    "    The INHD_MUNILIST lists all muni's already in the HD. (the popGap was already diminished by these added muni's to aDP)\n",
    "    We build out from closest to the \"closestTract_CenterPoint\" by full muni's until we pick a splittable muni that completes the HD\n",
    "     or adding whole muni's gets us to the target range.  We do not bias toward not splitting; this was already tried in another f'n\n",
    "    \"\"\"\n",
    "    CANDIDATELIST = C_MUNILIST.copy()\n",
    "    for m in CANDIDATELIST.copy():  #this could be imported as ALL of a county's muni's, so need to disqualify some ...\n",
    "        if m in INHD_MUNILIST or MUNIPOP[m] > 0.5*ADP:  #don't worry that this rejects all midsized muni's; they wouldn't fit anyway ...\n",
    "            CANDIDATELIST.remove(m)                    #... b/c we call this code only after we've added the closest whole muni's\n",
    "    addedPop = 0.\n",
    "    fillLIST3 = list()\n",
    "    addedMunis = INHD_MUNILIST.copy()\n",
    "    #print(\"candidate list, inHDalready muni lists are\",CANDIDATELIST, addedMunis)\n",
    "    while addedPop < TGTPOPf - TOLERf:  #each round, we look for neighbors of inHD muni's\n",
    "        nList = list()   #resetting the list of remaining available neighbor munis and their distances\n",
    "        nDistance = list()\n",
    "        if addedMunis == list():  #(Rare) Natural first muni to add from this county was too big to fit in. Below could reselect it.\n",
    "            #print(\"no muni's already in HD from this list.  Pick closest candidate\")\n",
    "            for m in CANDIDATELIST:\n",
    "                nList.append(m)\n",
    "                nDistance.append(TCPLIST[CT].distance(MUNIPCP[m]))\n",
    "        else: #we do have at least one muni already in the HD county remnant.  Only muni's contiguous to this list are in play\n",
    "            for mm in addedMunis:\n",
    "                for m in MUNINEIGHBORLIST[mm]:\n",
    "                    if m in CANDIDATELIST and m not in addedMunis and m not in nList:\n",
    "                        nList.append(m)\n",
    "                        nDistance.append(TCPLIST[CT].distance(MUNIPCP[m]) )\n",
    "        newMuni = nList[nDistance.index(np.min(nDistance))]  #closest contiguous candidate gets fully or partially added\n",
    "        addedMunis.append(newMuni)\n",
    "        if MUNIPOP[newMuni] > (TGTPOPf + TOLERf - addedPop):  #can't fit the whole muni in, so we find the partial to complete the HD\n",
    "            CUTPOINT = MUNIPCP[newMuni]\n",
    "            contiguousVTDs = list()\n",
    "            for MM in MUNINEIGHBORLIST[newMuni]:  #remember, muni's are not self-neighbors               \n",
    "                if MM in addedMunis:  #ensuring that at least one vtd in selected split muni is contiguous to addedMunis\n",
    "                    for VV in MUNITRACTLIST[MM]:\n",
    "                        for V in MUNITRACTLIST[newMuni]:\n",
    "                            if V in NEIGHBORLIST[VV] and V not in contiguousVTDs:\n",
    "                                contiguousVTDs.append(V)\n",
    "            includedVTD = getClosestTract(TCPLIST[CT], contiguousVTDs, TCPLIST) \n",
    "            ANGLE = getSliceAngle(TCPLIST[CT], MUNIPCP[newMuni],TCPLIST[includedVTD])\n",
    "            SUBLIST = MUNITRACTLIST[newMuni]\n",
    "            addedVTDlist, newPop = cutVtdSnap(TGTPOPf, TOLERf, TCPLIST, SUBLIST, NEIGHBORLIST, TRACTPOP, ANGLE, CUTPOINT)\n",
    "            addedPop += newPop\n",
    "            #print(\"added\", newPop,\"now our total getFillList3 pop is\",addedPop)\n",
    "            fillLIST3 = fillLIST3 + addedVTDlist\n",
    "            break\n",
    "        else: #we CAN fit the whole muni\n",
    "            addedPop += MUNIPOP[newMuni]\n",
    "            fillLIST3 = fillLIST3 + MUNITRACTLIST[newMuni]\n",
    "            #print(\"added a full muni, our total getFillList3 pop is\", addedPop)\n",
    "            \n",
    "    fillPOP = 0\n",
    "    for TT in fillLIST3:\n",
    "        fillPOP += TRACTPOP[TT]  #should = addedPop, but recalc just in case\n",
    "    return fillLIST3 , fillPOP #these are the vtd's we will add to the previous list of full muni's in the muni-snapped HD         "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2428025b-6146-46d6-abc1-2840e4e99e06",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#adding 3/19/23  - standardizing the voting mechanisms\n",
    "def getNonpickyPCvoters(candidatePCTlist, PLANS):\n",
    "    \"\"\"\n",
    "    Same as below, but all candidates found in at least one list are included\n",
    "    \"\"\"\n",
    "    PCTvotingList = list()\n",
    "    for v in candidatePCTlist:\n",
    "        foundInPlan = False\n",
    "        while not foundInPlan :\n",
    "            for plan in PLANS:\n",
    "                for districtList in plan:\n",
    "                    if v in districtList:\n",
    "                        foundInPlan = True\n",
    "                        PCTvotingList.append(v)\n",
    "                        break\n",
    "                if foundInPlan:  #move on to next candidate vtd\n",
    "                    break\n",
    "    return PCTvotingList\n",
    "\n",
    "def getPickyPCvoters(candidatePCTlist,  ALT_CP_LIST, TRACTCP, TRACTPOP, PLANS):\n",
    "    \"\"\"\n",
    "    A generalized approach for including/rejecting non-mainCounty tracts (from the candidatePCTlist)\n",
    "    in the group of \"voters\" on regional plans.  Tracts are included if they are found in a regional plan,\n",
    "    and they are better centered in the plan then in the global \"ALT_CP_LIST\",\n",
    "    which is usually the county population centerpoints. Each PLAN has a list of district tract lists\n",
    "    The method requires the ALT_CP_LIST to be passed for all vtd's; e.g. ALT_CP_LIST[t] = countyPCP[countyNo[t]]\n",
    "    \"\"\"\n",
    "    PCTvotingList = list()\n",
    "    for i, v in enumerate(candidatePCTlist):\n",
    "        altDistance = ALT_CP_LIST[i].distance(TRACTCP[v])  #.. alternative is usually centering in the county\n",
    "        bestDistance = altDistance\n",
    "        for plan in PLANS:\n",
    "            for districtList in plan:\n",
    "                if v in districtList:\n",
    "                    distance = get_PCP(districtList,TRACTCP, TRACTPOP).distance(TRACTCP[v])\n",
    "                    if distance < altDistance:\n",
    "                        bestDistance = distance\n",
    "            if bestDistance < altDistance:\n",
    "                break   #only need to find one qualifying plan, not the best\n",
    "        if bestDistance < altDistance:\n",
    "            PCTvotingList.append(v)  #we don't care here which plan is best.  A later loop will ID that\n",
    "\n",
    "    return PCTvotingList\n",
    "    \n",
    "def setPlanWeights(VOTERlist, PLANS, TRACTCP, TRACTPOP):\n",
    "    \"\"\"\n",
    "    This method assigns a relative voting weight to each of a list of regional district plans\n",
    "    Each vtd in the voter list selects the plan that minimizes their distance to a district in the plan,\n",
    "      selecting among plans that include them in one of that plan's districts\n",
    "    The voting weight of each plan is the fraction of VOTERlist pop that chose that plan\n",
    "    The VOTERlist could be all the vtd's in a single county, or also include vtd's from adjoining counties\n",
    "    The output is a 0-1 weight for each PLAN\n",
    "    \"\"\"\n",
    "    nVoters = 0.\n",
    "    nExcludedVoters = 0.  #in rare cases, an in-county tract is only in straddlers, doesn't vote on in-county plan\n",
    "    for v in VOTERlist:\n",
    "        nVoters += TRACTPOP[v]\n",
    "    nPlans = len(PLANS)\n",
    "    planWeight = [0.]*nPlans\n",
    "    dPCP = [list() for p in PLANS]  #district centerpt.  For broad lists, more efficient to calculate once\n",
    "    for p, plan in enumerate(PLANS):  #building the list of district centerpoints in each plan\n",
    "        for d in plan:\n",
    "            dPCP[p].append(get_PCP(d,TRACTCP, TRACTPOP))\n",
    "            \n",
    "    for v in VOTERlist:\n",
    "        minDist, bestPlan = 88888., -999\n",
    "        for p,plan in enumerate(PLANS):\n",
    "            for d,district in enumerate(plan):\n",
    "                if v in district:\n",
    "                    distance = dPCP[p][d].distance(TRACTCP[v])\n",
    "                    if distance < minDist:\n",
    "                        minDist = distance\n",
    "                        bestPlan = p\n",
    "        if bestPlan == -999:\n",
    "            print(\"WARNING! vtd\",v,\"not found in any of\",nPlans,\"regional plans\")\n",
    "            nExcludedVoters += tractPop[v]\n",
    "        else:\n",
    "            planWeight[bestPlan] += TRACTPOP[v] / float(nVoters)\n",
    "    if nExcludedVoters > 0:\n",
    "        nAdjVoters = nVoters - nExcludedVoters\n",
    "        for j in range(len(planWeight)):\n",
    "            planWeight[j] *= float(nVoters)/nAdjVoters\n",
    "    \n",
    "    if abs(np.sum(planWeight) - 1.) > 0.01:\n",
    "        print(\"WARNING! the plan weights sum to\",np.sum(planWeight),\"instead of unity\")\n",
    "    print(\"Voting done.  The most popular plan was\",planWeight.index(np.max(planWeight)),\"with\",r3(np.max(planWeight)),\"of the vote\")\n",
    "    return planWeight\n",
    "\n",
    "def set1DplanWeights(VOTERlist, DISTRICTS, TRACTCP, TRACTPOP):\n",
    "    \"\"\"\n",
    "    This method assigns a relative voting weight to each of a list of regional DISTRICTS (vs. above used plans of districts)\n",
    "    Each vtd in the voter list selects the DISTRICT that minimizes their distance to that district,\n",
    "      selecting among DISTRICTS that include them\n",
    "    The voting weight of each district is the fraction of VOTERlist pop that chose that district\n",
    "    The VOTERlist could be all the vtd's in a single county, or also include vtd's from adjoining counties\n",
    "    The output is a 0-1 weight for each DISTRICT\n",
    "    \"\"\"\n",
    "    nVoters = 0.\n",
    "    nExcludedVoters = 0  #in rare cases, an in-county tract is only in straddlers, doesn't vote on in-county plan\n",
    "    for v in VOTERlist:\n",
    "        nVoters += TRACTPOP[v]\n",
    "    nDs = len(DISTRICTS)\n",
    "    dWeight = [0.]*nDs\n",
    "    dPCP = [ get_PCP(d,TRACTCP, TRACTPOP) for d in DISTRICTS ] #building the list of district centerpoints in each plan\n",
    "\n",
    "            \n",
    "    for v in VOTERlist:\n",
    "        minDist, bestDistrict = 88888., -999\n",
    "        for d,district in enumerate(DISTRICTS):\n",
    "            if v in district:\n",
    "                distance = dPCP[d].distance(TRACTCP[v])\n",
    "                if distance < minDist:\n",
    "                    minDist = distance\n",
    "                    bestDistrict = d\n",
    "        if bestDistrict == -999:\n",
    "            print(\"WARNING! vtd\",v,\"not found in any of\",nPlans,\"regional plans\")\n",
    "            nExcludedVoters += tractPop[v]\n",
    "        else:\n",
    "            dWeight[bestDistrict] += TRACTPOP[v] / float(nVoters)\n",
    "    if nExcludedVoters > 0:\n",
    "        nAdjVoters = nVoters - nExcludedVoters\n",
    "        for j in range(len(dWeight)):\n",
    "            dWeight[j] *= float(nVoters)/nAdjVoters\n",
    "        \n",
    "    if abs(np.sum(dWeight) - 1.) > 0.01:\n",
    "        raise Exception(\"WARNING! the plan weights sum to\",np.sum(dWeight),\"instead of unity\")\n",
    "    print(\"Voting done.  The most popular district was\",dWeight.index(np.max(dWeight)),\"with\",r3(np.max(dWeight)),\"of the vote\")\n",
    "    return dWeight\n",
    "\n",
    "def classifyStraddlers(CHOOSERS, Alist, Blist, TRACTCP, TRACTPOP):\n",
    "    \"\"\"\n",
    "    This code takes a list of CHOOSERS (e.g. a straddling county's tractList) and partitions them into ...\n",
    "    vtd's that prefer to be drawn into a district in the Alist vs. the Blist, using the same principles as for set1DplanWeights;\n",
    "    i.e. the district that best centers this vtd. After classifying into 'Achoosers' and 'Bchoosers',\n",
    "    in later code blocks, these vtd's will vote alongside vtds in neighboring counties\n",
    "    \"\"\"\n",
    "    fusedList = Alist + Blist\n",
    "    dPCP = [ get_PCP(d,TRACTCP, TRACTPOP) for d in fusedList ] #building the list of district centerpoints\n",
    "    Achoosers, Bchoosers = list(), list()\n",
    "    for v in CHOOSERS:\n",
    "        minDist = 99999.\n",
    "        bestDno = -999\n",
    "        for dNo,district in enumerate(fusedList):           \n",
    "            if v in district: #must be in district to possibly vote for this district\n",
    "                distance = dPCP[dNo].distance(TRACTCP[v])\n",
    "                if distance < minDist: #closest to center so far\n",
    "                    minDist = distance\n",
    "                    best_dNo = dNo\n",
    "        if best_dNo != -999:  #at least one district contains this vtd\n",
    "            if best_dNo >= len(Alist):\n",
    "                Bchoosers.append(v)\n",
    "            else:\n",
    "                Achoosers.append(v)\n",
    "    return Achoosers, Bchoosers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "d4d53600-4ae4-4a9d-a014-301bf0a15bc0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9c1e657-06fd-43ca-8b9e-477ce71c855b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Feb'23 -- Our overall muni-snap approach:\n",
    "#1) Define functions above to perform subtasks for vtd-snapped and muni-snapped Home Districts\n",
    "#2) Below, region by region, create regional lists for both rule sets.  Designate plans as vtd-whole or muni-whole\n",
    "#3) Have all qualifying vtd's in each region vote on their favorite plan\n",
    "#4) Export each region's list of voted-on districts and their fractional votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "2a997ad1-b8f9-4f49-8345-9b21853099ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "112\n"
     ]
    }
   ],
   "source": [
    "print(len(AthensVotingList))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "222d73af-ad3e-4c55-be75-88697acc7168",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now working on Athens county 4. Add whole counties + one partial from neighbors only\n",
      "then ID tracts in neighbor counties with most Athens HDPoly pop to add them as voting tracts\n",
      "ready to draw Home Districts near Athens for total pop/aDP = 0.999\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "noncontiguous for t= 303\n"
     ]
    }
   ],
   "source": [
    "#2/26/23 -- *** Using Athens as code testing for creating muni-snapped tractLists.  THIS WORKS, BUT ... ***\n",
    "# 3/4/23  still works after modifying sliceAngle.  THIS CODE A BIT SKETCHY b/c it forces 119K people to have full AthensCo in their HD\n",
    "# 4/30/23 - correcting per 3/4/23's request\n",
    "# ALSO - NEED TO CAST THESE AS LISTS, LISTS3 A LA DAYTON.  Modify voting to match Lake\n",
    "\n",
    "print(\"now working on Athens county 4. Add whole counties + one partial from neighbors only\")\n",
    "print(\"then ID tracts in neighbor counties with most Athens HDPoly pop to add them as voting tracts\")\n",
    "mC = 4  #Athens is the \"main county\"\n",
    "aDP = avgDistrictPop\n",
    "toler = 0.05 * aDP\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "#pairedCountyList = neighborCountyList[mC]\n",
    "\n",
    "nearMCtractList = []\n",
    "totalNearMCpop = 0.\n",
    "for c in neighborCountyList[mC]:\n",
    "    for t in countyTractList[c]:\n",
    "        nearMCtractList.append(t)\n",
    "        totalNearMCpop += tractPop3[t]\n",
    "targetStraddlerPop = avgDistrictPop - countyPop[mC]\n",
    "nonMCVotingList = get_straddlers(targetStraddlerPop, totalNearMCpop, nearMCtractList, countyTractList[mC],\n",
    "                                countyGeom[mC],HDPoly, tractCP, tractPop3)\n",
    "AthensVotingList = countyTractList[mC] + nonMCVotingList\n",
    "AthensVotingPop = 0.\n",
    "AthensLISTS, AthensLISTS3 = list(), list()\n",
    "for t in AthensVotingList:\n",
    "    AthensVotingPop += tractPop3[t]\n",
    "print(\"ready to draw Home Districts near Athens for total pop/aDP =\",r3(AthensVotingPop/avgDistrictPop) )\n",
    "plotPoly(countyGeom[mC])\n",
    "for cc in neighborCountyList[mC]:\n",
    "    plotPoly(countyGeom[cc])\n",
    "for t in nonMCVotingList:\n",
    "    plotPoly(tractCP[t].buffer(0.02))\n",
    "plt.show()\n",
    "\n",
    "for t in AthensVotingList:\n",
    "    hC = countyNo[t]  #This tract's home county,  Note that some tracts are in Athens' neighboring counties.\n",
    "    HDtractList[t] = countyTractList[hC].copy()  #formerly mC\n",
    "    HDtractList3[t] = HDtractList[t].copy()\n",
    "    snappedHDpop[t] = countyPop[hC]    #formerly mC\n",
    "    usableCountyList = neighborCountyList[hC].copy()\n",
    "    #if hC != mC:  #this near-Athens tract is not in Athens County.  Force its entire county into the HD\n",
    "    #    snappedHDpop[t] += countyPop[countyNo[t]]\n",
    "    #    snappedHD3pop[t] = snappedHDpop[t]\n",
    "    #    HDtractList[t] = HDtractList[t] + countyTractList[hC]\n",
    "    #    HDtractList3[t] = HDtractList[t].copy()\n",
    "    #    usableCountyList.remove(mC)  #... since Athens already incorporated in HDTL\n",
    "        \n",
    "    countyFracUse = [0]*nCounties        \n",
    "    for cc in usableCountyList:\n",
    "            for tt in countyTractList[cc]:\n",
    "                overlap = HDPoly[t].intersection(tractGeom[tt]).area\n",
    "                if overlap > 0:\n",
    "                    countyFracUse[cc] += overlap/tractGeom[tt].area * tractPop3[tt]/ countyPop[cc]    \n",
    "    while snappedHDpop[t] < 0.95 * avgDistrictPop:\n",
    "        mostUsedCounty = countyFracUse.index(np.max(countyFracUse))\n",
    "        maxHDpopGap = 1.05 * avgDistrictPop - snappedHDpop[t]\n",
    "        #print(\"        t, picked county, its pop, maxHDpopGap are\",t,mostUsedCounty,countyPop[mostUsedCounty],r3(maxHDpopGap))\n",
    "        if countyPop[mostUsedCounty] < maxHDpopGap:  #add the full county; we won't go over\n",
    "            HDtractList[t] = HDtractList[t] + countyTractList[mostUsedCounty]      \n",
    "            HDtractList3[t] = HDtractList[t].copy()\n",
    "            snappedHDpop[t] += countyPop[mostUsedCounty]\n",
    "            snappedHD3pop[t] = snappedHDpop[t]\n",
    "            countyFracUse[mostUsedCounty] = 0.  #so we don't pick it again            \n",
    "        else:  #adding a final partial county.  Aim for 1.0 aDP.  Pizza-cut the county along a line perp'cular to beeline to hC PCP\n",
    "            pUC = mostUsedCounty  #this is our partially used county to complete the district\n",
    "            #print(\"tract\",t,snappedHDpop[t],\"= current HD pop. Add partial county\",pUC,\"with total countyPop\",countyPop[pUC])\n",
    "            mopupPop = aDP - snappedHDpop[t]\n",
    "            mopupPool = countyTractList[pUC].copy()  \n",
    "            cutPoint = countyPCP[pUC]\n",
    "            mCcT = getClosestTract(tractCP[t], mopupPool, tractCP)  #mopupCounty tract closest to the HD-centering tract\n",
    "            sliceAngle = getSliceAngle(tractCP[t], cutPoint, tractCP[mCcT])\n",
    "            #sliceAngle = sliceAngle + 0.5*pi  #TRY THIS ***********\n",
    "            #note: legislCounty code used debug_pieCut2 vs below\n",
    "            mopupList, mopupSnapPop = cutVtdSnap(mopupPop, toler, tractCP, mopupPool, neighborList, tractPop3, sliceAngle, cutPoint,mCcT)\n",
    "            #mCcT new in above\n",
    "            #print(\"I added\",mopupSnapPop,\"pop from county\",pUC,\". TolerPop was\",toler)\n",
    "            #plotPoly(countyGeom[mC])\n",
    "            #plotPoly(cutPoint.buffer(0.02))\n",
    "            #plotPoly(tractCP[mCcT].buffer(0.01))\n",
    "            #for v in mopupPool:\n",
    "            #    plotPoly(tractGeom[v])\n",
    "            #    if v in mopupList:\n",
    "            #        plotCenter(tractPop3[v], tractGeom[v])\n",
    "            #plt.show()\n",
    "            \n",
    "            mopupList3, fPML, mopupSnapPop3, splitMuni = cutMuniSnap(mopupPop, toler, muniTractList,tractCP, mopupPool, muniNeighborList, \n",
    "                                                                     tractPop3, muniPop, muniPCP, sliceAngle, cutPoint)\n",
    "            fillList3, fillPop3 = [],0  #additional vtd's needed to fill pop for some muni-snapped HDs\n",
    "            if snappedHDpop[t] + mopupSnapPop3 < avgDistrictPop - toler : #need to split a muni or add alt muni's to complete\n",
    "                popGap = avgDistrictPop - snappedHDpop[t] - mopupSnapPop3\n",
    "                if keepWholeMuni[splitMuni] == 1:  #need to find an alternate muni(s) to complete the district\n",
    "                    fillList3, fillPop3 = getFillList3(mCcT, popGap, toler, aDP,muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                       fPML,countyMuniList[pUC],tractCP, neighborList, tractPop3)\n",
    "                else:                    \n",
    "                    splitMuniClosestTract = getClosestTract(tractCP[t], muniTractList[splitMuni], tractCP)\n",
    "                    fillList3, fillPop3 = cutVtdSnap(popGap, toler,tractCP, muniTractList[splitMuni], neighborList, tractPop3,\n",
    "                                                    sliceAngle, cutPoint, splitMuniClosestTract)         \n",
    "            HDtractList3[t] = HDtractList[t].copy() + mopupList3 + fillList3\n",
    "            snappedHD3pop[t] = snappedHDpop[t] + mopupSnapPop3 + fillPop3\n",
    "            HDtractList[t] += mopupList\n",
    "            snappedHDpop[t] += mopupSnapPop\n",
    "            #fullCountyPoly = tractCP[t]\n",
    "            #plotPoly(countyGeom[mC])\n",
    "            #for tt in HDtractList[t]:\n",
    "            #    fullCountyPoly = fullCountyPoly.union(tractGeom[tt])\n",
    "            #    plotPoly(tractGeom[tt])\n",
    "            #plotPoly(fullCountyPoly)\n",
    "            #plotPoly(tractCP[t].buffer(0.1))\n",
    "            #plotPoly(tractCP[mCcT].buffer(0.03))\n",
    "            #plotCenter(t,tractCP[t])\n",
    "            #plt.show()\n",
    "            #plotPoly(fullCountyPoly)\n",
    "            #plotPoly(tractCP[t].buffer(0.1))\n",
    "            #DR = 0.2\n",
    "            #plt.plot([cutPoint.x - DR * math.cos(sliceAngle), cutPoint.x + DR * math.cos(sliceAngle)],\n",
    "            #         [cutPoint.y - DR * math.sin(sliceAngle), cutPoint.y + DR * math.sin(sliceAngle)] ) #visualizing the cut\n",
    "            #    plotPoly(tractGeom[tt3])\n",
    "            #print(t,\"above was vtd-snapped, below is muni-snapped.  Final pops were\",int(snappedHDpop[t]),snappedHD3pop[t])  #debug\n",
    "            #plt.show()\n",
    "            if isContiguous(HDtractList[t], neighborList) and isContiguous(HDtractList3[t],neighborList):\n",
    "                if max(abs(snappedHDpop[t] - aDP), abs(snappedHD3pop[t] - aDP) ) < 0.05* aDP:\n",
    "                    AthensLISTS.append(HDtractList[t])\n",
    "                    AthensLISTS3.append(HDtractList3[t])\n",
    "                else:\n",
    "                    print(\"missed pop for\",t,snappedHDpop[t],snappedHD3pop[t])\n",
    "            else:\n",
    "                print(\"noncontiguous for t=\",t)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "ae15293e-312d-46be-8ddb-72a44e6c3fea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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V0mpzzNdwCkpTjW0S1EYCGpWN+Nexfzn0Hl8c2gql7v8Q4lcrOx7sVwul7v/wxaGt2F2yG5lfZcqSIACoaqpC5leZ2F2y26H3coohky2rwzq8AxIQEmOJI/JiTITIymw2Iysrq9OYrKwsmM3mXmoRkWOi/Gw7txWmOodeW64v7zKm1dSKxuoNAID2C9AUkmXqcUP1Bqw/uBbCzuTktmPrD66HycEaPjdMobQskQfQ4Y6ps9dxA1ryekyEyKqkpMSmJ6g9vV6PkpKSXmoRkWMmhgZhgMpX9nVvVoY69NoBIQO6jMn775dQq2ptkqA2CgkIVdUiyNRxUiUgUNFUgSNVRxxql1PEplqWyLf/jCHabi2dJ+rLOEeIrBoaGpwaR9RblJKE3wyPwSOF59qm/8CoGgGTMhyKjobHBBBoDsScUXO6vL6uoRz9HGhHiLLrsmzVTdUOXMmJYlMtS+RL9lsmRgdFW4bD2BNEBIA9QnSNoKAgp8YR9abkyFC8FTcUGtWVic+SAg1hD1j+3MH2EI/d8phDE6XVQV33GgGA3tR14cbIgEiHruVUCqVlifyoeyz/ZRJEZMUeIbIaMmQIQkJCOh0eCwkJwZAhQ3qxVUSOS44MxewI9TWVpW/Cf4/74I+n30CTsskaF2gOxGO3PObw0vmJP7wTO0vDEOxXC4WdXMcsAH1LGBqUakiotjtPSIKE6IBoxEfFX/fnIyLn4xYbXfC2LTbaVo11ZN68eVxCTx7H2GrEv47964YqS7etGhOALBkyC8vUY5N6NZSREcj8KhMAZMmQdGVwbuO0jb27hJ7Iizn6/c1EqAvelggBrCNE1JFr6wi1qTOEIaiLOkKaAA2WjF/CJIioFzERchJvTIQAVpYm6ojHVJYm8nJMhJzEWxMhIiIiT+bo9zd/xSciIiKvxVVjRER9GIfpiDrHRIiIqI9ymw1gidwYh8aIiPogt9oAlsiNdTsRysnJQUpKCrRaLSRJwo4dO6znjEYjlixZglGjRiEwMBBarRYPPvggysrKrDE1NTV46qmnMGLECAQEBGDw4MFYtGgRdDqd7H2GDh0KSZJkj6VLl8piSktLkZKSgsDAQERERGDRokVoaWmRxRw7dgwJCQnw9/dHTEwMVq1aBc4PJ6K+zGQ2Yd3Bde6zASyRG+v20FhjYyNGjx6Nhx56CHfffbfsXFNTE44cOYIXXngBo0ePRm1tLZ555hmkpqbi0KFDAICysjKUlZVhw4YNiI2NRUlJCR5//HGUlZXho48+kl1v1apVyMjIsD6/dmsHk8mE5ORkREZGYt++fbh06RLmz58PIQQ2bdoEwDJjfNasWZg+fTry8/Nx+vRpLFiwAIGBgVi8eHF3PzoRkUc4UnXEpifoWtduAHuH5o5ebBmR++l2IjRnzhzMmWN/k0K1Wo1du3bJjm3atAnjx49HaWkpBg8ejLi4OHz88cfW8zfddBNWr16NBx54AK2trfDxudqk4OBgaDQau++VnZ2NEydO4Pz589BqtQCAl19+GQsWLMDq1asREhKC9957D83NzdiyZQtUKhXi4uJw+vRpbNy4EZmZmZA62kqaiMiDObqxa69vAEvkhnp8jpBOp4MkSQgNDe00JiQkRJYEAcD69evRv39/3H777Vi9erVs2Cs3NxdxcXHWJAgAkpKSYDAYcPjwYWtMQkICVCqVLKasrAznzp2z2xaDwQC9Xi97EPVFZrMJ549/i5Pf/Bvnj38LM4dJ+gxHN3Z1yQawRG6mR1eNNTc3Y+nSpUhLS+uwmNGlS5fw61//Go899pjs+NNPP434+HiEhYXh4MGDWLZsGYqLi/HWW28BACoqKhAdHS17TVhYGPz8/FBRUWGNGTp0qCym7TUVFRUYNmyYTXvWrl2LlStXXtfnJfIURQf248stf0RDzUXrsaDwCNy54FEMnzDZhS0jZ4iPikd0QDSqmqq4ASxRF3qsR8hoNOK+++6D2WzGa6+9ZjdGr9cjOTkZsbGxWLFihezcs88+i4SEBNx222145JFH8MYbb+Dtt9/GpUuXrDH2hraEELLj7WPaJkp3NCy2bNky6HQ66+P8+fOOfWAiD1F0YD92blwjS4IAoKHmInZuXIOiA/td1DJyFqVCiaXjLYtL2jZ8bdP2fMn4JX2inpChuQm7/rIC/9j8C+z6ywoYmptc3STyMD2SCBmNRsybNw/FxcXYtWuX3d6g+vp6zJ49G0FBQdi+fTt8fTvfCXrixIkAgDNnzgAANBqNteenTW1tLYxGo7XXx15MVVUVANj0JrVRqVQICQmRPYj6CrPZhC+3/LHTmL1/+SOHyfqAmUNmYuO0jYgKiJIdjw6IxsZpG/tEHaF/vv5L7P1iDBSD3kVQ7L+hGPQu9n4xBv98/Zeubhp5EKcPjbUlQUVFRdi7dy/69+9vE6PX65GUlASVSoWdO3eiX79+XV736NGjAIABAwYAACZNmoTVq1ejvLzceiw7OxsqlQpjx461xixfvhwtLS3w8/Ozxmi1WpshMyJvcOHkcZueoPbqL13EhZPHMejW23qpVdRTZg6ZiemDpvfJytL/fP2X6HdLts1xn6BW+NySjX++/kvM/eXrLmgZeZpuJ0INDQ3WXhkAKC4uRkFBAcLDw6HVanHPPffgyJEj+Oc//wmTyWTtkQkPD4efnx/q6+uRmJiIpqYmvPvuu7IJyZGRkVAqlcjNzUVeXh6mT58OtVqN/Px8PPvss0hNTcXgwYMBAImJiYiNjUV6ejp+97vfoaamBs899xwyMjKsvThpaWlYuXIlFixYgOXLl6OoqAhr1qzBiy++yBVj5JUa6mqdGkfuT6lQ9rkl8obmJvhovwQAtP9RLkmAEICP9ksYmpug6hfgghaSJ+l2InTo0CFMnz7d+jwzMxMAMH/+fLz00kvYuXMnAOD222+XvW7v3r2YNm0aDh8+jAMHDgAAbr75ZllMcXExhg4dCpVKhQ8//BArV66EwWDAkCFDkJGRgV/96lfWWKVSic8++wxPPPEEpkyZAn9/f6SlpWHDhg3WmLbl/E8++STGjRuHsLAwZGZmWttM5G2CQsOcGkfkCjkfrofvoNYOz0sS4BvcipwP12PWfC5+oc51OxGaNm1ap5WZu6ra3NXrASA+Ph55eXldtmXw4MH45z//2WnMqFGjkJOT0+W1iLxBzMhbERQe0enwWHD/CMSMvLUXW9XzzGaB8qI6NOoNCAxRYcDwUCgU7BX2VE315xHUdRia6rnYhbrGTVeJvIhCocSdCx7Fzo1rOoyZPv9RKPrAHJI2Z49W4esPi9BYZ7AeCwxVYeq9w3HTmKhOXknuKiB4kFPjyLtx01UiLzN8wmSkZi5HUHiE7Hhw/wikZi7vU3WEzh6tQtabhbIkCAAa6wzIerMQZ49WuahldCN+fO8SGOt90NHgghCAsd4HP753Se82jDwSe4SIvNDwCZNx0x0TLKvI6moRFBqGmJG39qmeILNZ4OsPizqN2betCMNGR3KYzMOo+gWgtexO+NySDSHkE6bbkqPWsjs5UZocwh4hIi+lUCgx6NbbMHJKAgbdelufSoIAWOYEtesJaq+h1oDyorreaRA51dxfvo7m04lobZD/Pt/a4IPm04lcOk8OY48QEfVJjfrOk6DuxpH7mfvL12FobkLOh+vRVH8eAcGDMP3eJewJom5hIkREfVJgiKrroG7EkXtS9QvgEnm6IUyEiKhPGjA8FIGhqk6Hx4LCLEvpvYEQJtTV5cNgqIJKFYXQ0DsgSX1rOJToejARIqI+SaGQMPXe4ch6s7DDmB/NG+4VE6Wrqr7A6aJVMBiu7r2oUmlwy/AXERWV5MKWEbkeJ0sTUZ9105gozH4sDoGh8uGvoDAVZj8W5xV1hKqqvsCxwidlSRAAGAyVOFb4JKqqvnDae7W2mlGwuxT/3noKBbtL0dpqdtq1iXqKJLoq8+zl9Ho91Go1dDodd6InusJsNnnU0ntvqCzd0mLA1+99grqKMoRqtJh6/13w9fXBN/t/bJMEXSVBpdJgyuR/3/Aw2TcfF+E/u8/LavtIEjB65iBMuXv4DV2b6Ho4+v3NoTEi6paiA/vx5ZY/yrbpCAqPwJ0LHnXbYowKhYSYEX13/7R/vLwZZw5+AeBqFlKQ9T5G3BkL/+EdJUEAIGAwlKOuLh9hYROv+/2/+bgIBbtst7MQAtbjTIbIXXFojIgcVnRgP3ZuXGOzV1lDzUXs3LgGRQf2u6hl3suSBGXh2iTIQqCyxLH/HwZDNypsm01A8dfAsY+A4q/R2mLEf3Z3vqfXf3af5zAZuS32CBGRQ8xmE77c8sdOY/b+5Y+46Y4Jbj1M1pe0tBiu9ATZ19rk2I94lcrBuVIndgJZSwB9mfWQ2S8aw/zm4zvDpA5fJgRQ+NX3uH3mYMfeh6gXsUeIiBxy4eTxTnetB4D6Sxdx4eTxXmoRff3eJ7DtCbqqoSIALQ0+nYRIUKkGIDT0jq7f7MROYNuDsiQIAHxbKjE79Lf4gSq305frLl7u+j2IXICJEBE5pKGu1qlx7kiYTGg8cBC6f36GxgMHIUwmVzepU3UVZZ0HCAkX9kdfyYPaTw63PL9l+AtdT5Q2myw9QXYyqrar/ijkHUjo+H6pI/w7fw8iF+HQGBE5JCjUscnGjsa5G312NirXrEVrxdXJxT4aDaKXL0NIYqILW9axUI22yxhdcQik6h9DNWivnTpCLzhWR6hkv01P0LUkCQhWXsQAv5Moa4mzez5u2sCu34fIBZgIEZFDYkbeiqDwiE6Hx4L7RyBm5K292Crn0Gdn48LTzwDtqom0VlZajv/+VbdMhqbefxcKst5HZ8NjgISpd70AX9+V119ZuqHSobBAhf3ewNEzB8HHhwMQ5J74N5OIHKJQKHHngkc7jZk+/1GPmChtMgvknr2EfxRcQG5RFcrWrLNJggBYj1WuWeuWw2R+fircPL7zHp2bxyfBz08FSVIiLGwiNJpUhIVN7F7doKBoh8KazPLeQEkCbp/FOkLk3lhQsQssqEgkZ6+OUHD/CEyf7751hK6VVViOlZ+eQLmu2Xos4nIdHv92B6aUd7wdx+C//AWBE8b3RhO7zV4dIUDCzeOT8JPFC2/8Dcwm4NU4QF8O+71PEhCiRevC/6Awpxy6i5ehjvBH3LSB7Akil3H0+5uJUBeYCBHZ8rTK0m2yCsvxy3eP2H6VX/kx+PzBv3SYDGk3bIB6bnLPNvAG2Kss7een6vqFjmpbNQagfcIFAJj3VyA21XnvR3SDWFmaiHqMQqHEoFtvc3UzusVkFlj56Qn7s2kkCRACb476CSaWH4fSTpRPZGSPt/FG+PmpMOOhn/fcG8SmWpKddnWEEKIFZq9jEkQei4kQETlMmAUMxTqY61ugCPaDapgakofs2XWwuEY2HGZDklAdEIbjET/AbRfPyo77REcjYNzYnm9kDxDCdP2TpNuLTQV+mGxZRdZQaZk7NGQy4AG9gUQdYSJERA65XHgRdZ+ehUnXYj2mVPshNOUm+MdFuLBljqmq7yQJukaNKvjqE8mS5EUvXwZJ6Xlf9lVVX+B00So7y+ZfdGzZvD0KJTBsqpNaSOR6nMVGRF26XHgRl949KUuCAMCka8Gld0/icmHnFafdQVRwP4fiwg311j/7REcjxk2XznelquoLHCt80mbneYOhEscKn0RVVcdbcxB5E/YIEVGnhFmg7tOzncbUffod+sX2d+thsvHDwjFA3Q8VuuaO1j1Bo+6HORtegLh4ET6RkQgYN9Yje4KEMOF00SrYX+ElAEg4XfRrREbOvP5hMqI+gj1CRNQpQ7HOpieoPZPOAEOxrpdadH2UCgkrUmIBdLTZBLAiJRYhEydAPTcZgRPGe2QSBODKnKCKTiIEDIZy1NXl91qbiNwVEyEi6pS5vvMkqLtxrjQ7bgBefyAeGrV8mEyj7ofXH4jH7LgBLmpZ95jNZhQXF+PYsWMoLi6G2WyWnTcYqhy6jqNxRH0Zh8aIqFOKYD+nxrna7LgBmBWrwcHiGlTVNyMquB/GDwuH0o2H9a514sQJZGVlQa/XW4+FhIRg9uzZiI219HipVFEOXcvROKK+jIkQkZsxmwXKi+rQqDcgMESFAcNDoXDhl7RqmBpKtV+nw2NKtQqqYepebNWNUSokTLqpv6ub0W0nTpzAtm3bbI7r9Xps27YN8+bNQ2xsLEJD74BKpYHBUImOKkGrVBqEht7R420mcndMhIjcyNmjVfj6wyI01hmsxwJDVZh673DcNMY1v71LCgmhKTfh0rsnO4wJTfmBW0+U7gvMZjOysrI6jcnKysIPf/hDKBRK3DL8RRwrfBKWGVC2laBvGf4CJ0oTgXOEiNzG2aNVyHqzUJYEAUBjnQFZbxbi7FHXzefwj4tA/wdGQqmWD38p1Sr0f2CkR9QR8nQlJSWy4TB79Ho9SkpKAABRUUkYFfcHqFTyDVNVKg1Gxf3h+usIEfUx7BEicgNms8DXHxZ1GrNvWxGGjY7scpjMZBY9Mv/FPy4C/WL7e2xlaU/X0NDQ7bioqCRERs50XmVpoj6IiRCRGygvqrPpCWqvodaA8qI6xIwI6zDG3s7qA9T9sCIl1ikroiSFhH43hd7wdaj7goKCritOkpQIC5vYE00i6hM4NEbkBhr1nSdBjsS17azefj+tCl0zfvnuEWQVlt9QG8m1hgwZ0ukO2oBl9diQIUN6qUVEfQMTISI3EBiiuqG4znZWbzu28tMTMJnt7r1OHkChUGD27NmdxsyePRsKBX+sE3UH/8UQuYEBw0MRGNp5MhQUZllKb09XO6sLAOW6ZhwsrrmBVpKrxcbGYt68eTY9QyEhIdal80TUPZwjROQGFAoJU+8djqw3CzuM+dG84R1OlHZ0Z3VH48h9xcbG4oc//CFKSkrQ0NCAoKAgDBkyhD1BRNep2/9ycnJykJKSAq1WC0mSsGPHDus5o9GIJUuWYNSoUQgMDIRWq8WDDz6IsrIy2TUMBgOeeuopREREIDAwEKmpqfj+++9lMbW1tUhPT4darYZarUZ6ejrq6upkMaWlpUhJSUFgYCAiIiKwaNEitLTIi74dO3YMCQkJ8Pf3R0xMDFatWgUhODxA7uemMVGY/VicTc9QUJgKsx+L67SOkKM7qzsaRzeupaUV2/6Rj1f/vBfb/pGPlpZWp11boVBg2LBhGDVqFIYNG8YkiOgGdLtHqLGxEaNHj8ZDDz2Eu+++W3auqakJR44cwQsvvIDRo0ejtrYWzzzzDFJTU3Ho0CFr3DPPPINPP/0UW7duRf/+/bF48WLMnTsXhw8fhvLKJodpaWn4/vvvrQXEHn30UaSnp+PTTz8FAJhMJiQnJyMyMhL79u3DpUuXMH/+fAghsGnTJgCWmhqzZs3C9OnTkZ+fj9OnT2PBggUIDAzE4sWLr++OEfWgm8ZEYdjoyG5XlnZ0Z/Xxw8J7pN0k9/q7OdhceAmNaKu71ISVuZ9iYVx//PKBH7u0bUQkJ4kb6B6RJAnbt2/HT3/60w5j8vPzMX78eJSUlGDw4MHQ6XSIjIzE3/72N9x7770AgLKyMgwaNAiff/45kpKScPLkScTGxiIvLw8TJkwAAOTl5WHSpEn473//ixEjRuBf//oX5s6di/Pnz0Or1QIAtm7digULFqCqqgohISF4/fXXsWzZMlRWVkKlsvyWvW7dOmzatAnff/89JKnr+id6vR5qtRo6na7LFRtErtS2agywV0cYHrWpqCd7/d0crC9sK3x47c8Yy/+VJXEhTIaIeoGj39893p+q0+kgSRJCQ0MBAIcPH4bRaERiYqI1RqvVIi4uDvv37wcA5ObmQq1WW5MgAJg4cSLUarUsJi4uzpoEAUBSUhIMBgMOHz5sjUlISLAmQW0xZWVlOHfuXE99ZCKX6Cs7q7sbYTKh8cBB6P75GRoPHIQwmTqMbWlpxebCS1eetf9Fy/L8D4WXnDpMRkQ3pkcnSzc3N2Pp0qVIS0uzZmMVFRXw8/NDWJi8KFx0dDQqKiqsMVFRtvMhoqKiZDHR0fLS8WFhYfDz85PFDB061OZ92s4NGzbM5j0MBgMMhqu1WroqaU/kTjx9Z3V3o8/ORuWatWi98jMFAHw0GkQvX4aQa36Za7PjX0evGQ6zR0ID/LDjX0cx7yfc8JTIHfRYImQ0GnHffffBbDbjtdde6zJeCCEbqrI3bOWMmLaRwI6GxdauXYuVK1d22V4iZ3H2lhieurO6u9FnZ+PC088A7WYPtFZWWo7//lWbZKisxrFtMByNI6Ke1yNDY0ajEfPmzUNxcTF27dolG5vTaDRoaWlBbW2t7DVVVVXW3hqNRoPKykqb61ZXV8tiKq75LQ2wrDQzGo2dxlRVWTaubN+b1GbZsmXQ6XTWx/nz57vz0Ym6JauwHD9a/yV+/qc8PL21AD//Ux5+tP5LVoF2MWEyoXLNWpskyHLScqxyzVqbYTJtuGPbYDgaR0Q9z+mJUFsSVFRUhN27d6N/f/lvpmPHjoWvry927dplPVZeXo7CwkJMnjwZADBp0iTodDocPHjQGnPgwAHodDpZTGFhIcrLr35hZGdnQ6VSYezYsdaYnJwc2ZL67OxsaLVamyGzNiqVCiEhIbIHUU/glhjuq+nQYdlwmA0h0FpRgaZDh2WHfzpnDALRAthduwcAAkFowU/njHFaW4noxnQ7EWpoaEBBQQEKCgoAAMXFxSgoKEBpaSlaW1txzz334NChQ3jvvfdgMplQUVGBiooKazKiVqvx8MMPY/HixdizZw+OHj2KBx54AKNGjcLMmTMBACNHjsTs2bORkZGBvLw85OXlISMjA3PnzsWIESMAAImJiYiNjUV6ejqOHj2KPXv24LnnnkNGRoY1eUlLS4NKpcKCBQtQWFiI7du3Y82aNcjMzHRoxRhRT/G2LTFMQuCb2npsr6zFN7X1MLl5La/W6urrivPz88HCuLZf/tp/RsvzJ+P6w8+PtWyJ3EW3/zUeOnQI06dPtz7PzMwEAMyfPx8vvfQSdu7cCQC4/fbbZa/bu3cvpk2bBgB45ZVX4OPjg3nz5uHy5cuYMWMGtmzZYq0hBADvvfceFi1aZF1dlpqais2bN1vPK5VKfPbZZ3jiiScwZcoU+Pv7Iy0tDRs2bLDGqNVq7Nq1C08++STGjRuHsLAwZGZmWttM5Crd2RLD0+f7fFZdh+eLLqDcYLQeG6DyxW+GxyA5MtR1DeuET2Tkdcf98oEfAzZ1hIAgGPEk6wgRuZ0bqiPkDVhHiHrCPwou4OmtBV3G/f6+2/GT22N6vkE95LPqOjxSeM6mb6StP/atuKFumQwJkwlnZsxEa2Wl/XlCkgSf6GjcvGc3pGt+gbtWS0srdvzrKMpqGqAND8JP54xhTxBRL3L0+5v/KolcwBu2xDAJgeeLLnQ4/CcBeKHoAmZHqKF00VC10WTCzlPf4fumJgwMCEDqiB/AV6mEpFQievkyy+owSZInQ1faGr18WYdJEGAZJuMSeSL3xw1qiFygbUuMjr7+JQADPHxLjLy6BtlwWHsCQJnBiLw61ywlf+NIIWJ35+PJykasrRd4srIRsbvz8cYRy8a3IYmJiPn9q/Bpt8LUJzoaMXaWzhORZ2KPEJELKBUSVqTE4pfvHoEE+1tirEiJ9ehCiFUOVk92NM6Z3jhSiJfqjICvfIPbel+V5fiRQjweH4eQxEQEz5hhWUVWXQ2fyEgEjBvbaU8QEXkW9ggRuUhf3xIjysH5MI7GOYvRZMLL1Vd6odoPyV15/nJ1A4xXagRJSiUCJ4yHem4yAieMZxJE1MewR4jIhfrylhgTQ4MwQOWLCoPR7jwhCZbVYxNDe7e44M5T36Her5O5V5KEer9+2HnqO9wdO7z3GkZELsFEiMjF+uqWGEpJwm+Gx+CRwnMdDv/9eniMUydKC7OAoVgHc30LFMF+UA1TQ2qXVH7f1OTQtRyNIyLPxkSIiHpMcmQo3oobareO0K+dXEfocuFF1H16Fibd1UrySrUfQlNugn9chPXYwIAAoL6xy+sNDAhwWtu6w9RqRunXF9B48TICI/wxeGoMlD6cxUDUU1hHqAusI0R040xCIK+uAVUtrYjy88HE0CCn9gRdLryIS++e7PB8/wdGWpMho8mE2N35qPdV2c4RAgAhEGw04MTMO+Dby/OBTv7jLHKzS3HZdPXHsr9SwqTEwRj5k5t6tS1Eno51hIjIbSglCVPCgp17UbMJKNkPUV+Bpk9rAdwCwH7iUvfpd+gX2x+SQoKvUonFkUGW1WFCyJOhK78XLo4MckkS9OW/SmyOXzYJ63EmQ0TOx0SIiDzPiZ1A1hJAXwYJQH8AraoI1BkfRbN5sk24SWeAoViHfjeFAgAej48DjhTi5eoG2cTpYKMBiyODLOd7kanVjNzs0k5jcrNLcUvyMA6TETkZEyEi8iwndgLbHkT7TU2VuIj+vmtwybjcbjJkrm+RPX88Pg4P21SWHtXrPUEAUPr1BdlwmD2XTQKlX1/AsOmDeqlVRN6BiRAReQ6zydITZGdBfttOGKG+f0SFYQLaD5Mpgv1sXuOrVLrFEvnGi5edGkdEjmMfKxF5jpL9gL6sw9OSBPhIF6FSHJcdV6pVUA1T93TrrltghL9T44jIcUyEiMhzNFQ6FKZArex5aMoPbOoJuZPBU2Pgr+y8ff5KCYOnxvRSi4i8BxMhIvIcQdFdxwAwIwyApSfo2qXz7krpo8CkxMGdxkxKHMyJ0kQ9gHOEiMhzDJkMhGgBfTnszRMSkIAADQJ/cheCQ/ztVpZ2V21L41lHiKh3MREiIs+hUAKz119ZNWa7cYcEAHN/i4DYqxvW9nQxR2ca+ZObcEvyMFaWJupFTISIyLPEpgLz/mqtI2QVogVmr7Ocv+Kz6jq723v8xsnbeziT0kfBJfJEvYhbbHSBW2wQuakrlaXRUGmZOzRksqXH6IrPquvwSOE5mwG0tr6gt+KGOpYMdfE+ROSeuMUGEfVtCiUwbKrdUyYh8HzRBTuziCyDaRKAF4ouYHaEuvNhsmsqWFuFaC3Dc9f0PBGR5+LAMxH1OXl1DbLhsPYEgDKDEXl1DR1fpK2Cdfu6Rfpyy/ETO53TWCJyKSZCRNTnVLW03lhcJxWsrceyllriiMijMREioj4nys+xUf8O47qoYA0IQH/BEkdEHo1zhIjcBSflOs3E0CAMUPmiwmC026cjwbJ6bGJokP0LOFjB2uE4InJbTISI3AEn5TqVUpLwm+ExeKTwHK5Oj75CmCEg4WehlzqeKO1gBWuH44jIbXFojMjVOCm3RyRHhmJRtB6K1hrZcYWpFiEXN+GD/IXYXbLb/ovbKlijoxVlEhASY4kjIo/GHiEiV+pyUq5kmZT7w2QOk3WTyWzCnuOrEN5UBaNqBMzKUChMdfA1nIJ05d6uP7ge0wdNh7L9ve2igjUAS/HGXvh/YmxtxZ69ebhYVY2IqEjMmD4Rvj780U3kLPzXRORK3ZmU20HNHLLvSNURVDZVQgLgZ/ivzXkBgYqmChypOoI7NHfYXqAbFax7yra/f4ZT//gbAoyWZf6VAPL/EoQRP0nHvJ8l9/j7E3kDJkJErsRJuT2muqn6xuNiUy29cS6YxL7t75+h9KPX4d/uuL+xAaUfvY5tAJMhIidgIkTkSpyU22MiAyKdE9dJBeueYmxtxal//A3+sJ2l1DZQd+off4Pxf5M4TEZ0gzhZmsiVOCm3x8RHxSM6ILptT3obEiRoAjSIj4rv5ZZ1bc/ePAQYGzr7W4EAYwP27M3rzWYR9UlMhIhcqW1SLgD7v/uj1ybl9jVKhRJLxy8FAJtkqO35kvFLbCdKu4GLVY4N6zkaR0QdYyJE5Gptk3JDBsiPh2gtx1lH6LrNHDITG6dtRFRAlOx4dEA0Nk7biJlDZrqoZZ2LiHJsWM/ROCLqmCSEsLdul67Q6/VQq9XQ6XQICQlxdXOoL2Nl6R5jMptwpOoIqpuqERkQifioeLfsCWpjbG3FugUPwL+D4TEB4LJvEJZueZdzhIg64Oj3N/8FEbkLF0zK9RZKhdL+Enk35evjgxE/SUfpR6+3r4ttrWg04ifpTIKInID/ioj6KGNTAw5t/hXqK84jWDMI4xb+Fr4BHeytRW5n3s+SsQ2Q1RECLD1BrCNE5DwcGusCh8bIE+1Zeg/8dx9H2NXvT9QGAZdn3ooZ6z5yXcOo21hZmuj6OPr9zUSoC0yEyNPsWXoPBuw4DkA+pGK+8rz8p0yGiKjvc/T7u9urxnJycpCSkgKtVgtJkrBjxw7Z+U8++QRJSUmIiIiAJEkoKCiQnT937hwkSbL7+Pvf/26NGzp0qM35pUuXyq5VWlqKlJQUBAYGIiIiAosWLUJLS4ss5tixY0hISIC/vz9iYmKwatUqMPejvsrY1AD/3bZJEGD5xy4A9Nt9HMamhvYvJSLySt1OhBobGzF69Ghs3ry5w/NTpkzBunXr7J4fNGgQysvLZY+VK1ciMDAQc+bMkcWuWrVKFvf8889bz5lMJiQnJ6OxsRH79u3D1q1b8fHHH2Px4sXWGL1ej1mzZkGr1SI/Px+bNm3Chg0bsHHjxu5+bCK30WpuxRelB/HnU7vxRelBtJpbrecObf4Vwho6Ls+oABDeYIkjIqLrmCw9Z84cm4TlWunp6QAsPT/2KJVKaDQa2bHt27fj3nvvRVCQfCJncHCwTWyb7OxsnDhxAufPn4dWqwUAvPzyy1iwYAFWr16NkJAQvPfee2hubsaWLVugUqkQFxeH06dPY+PGjcjMzIQkdfR1QeSe3j/zFVafF7iEMAARAID+Z7/G/w2SkHbzNNRXnEeoA9eprzh/Xe/fcrkBX3yyBJWN3yM6cCCS7loPP39OwCYiz+XygoqHDx9GQUEBHn74YZtz69evR//+/XH77bdj9erVsmGv3NxcxMXFWZMgAEhKSoLBYMDhw4etMQkJCVCpVLKYsrKyDhM1g8EAvV4vexC5g/fPfIXMUjUuiVDZ8UtCjcxSNd4/8xWCNYMcupajcdf661s/w4y/T8Dy1hz8XvUdlrfmYMbfJ+Cvb/2s29ciInIXLk+E3n77bYwcORKTJ8v3Unr66aexdetW7N27FwsXLsSrr76KJ554wnq+oqIC0dHyjSjDwsLg5+eHioqKDmPanrfFtLd27Vqo1WrrY9Cg7n9hEDlbq7kVq89fmdvWvidTssz+WX3ejNufWIPaIMvEaHvMAGqCgHELf9ut9//rWz/D73xPok7I37tOSPid70kmQ0TksVyaCF2+fBnvv/++3d6gZ599FgkJCbjtttvwyCOP4I033sDbb7+NS5cuWWPsDW0JIWTH28e0TZTuaFhs2bJl0Ol01sf589c3hEDkTHu+P2IZDutoOFdS4BLCkVNzGpdn3goJtslQ26qx5pm3dqueUMvlBvzJ70TbG7V/YwDAn/xOoOUyJ2ATkedxaSL00UcfoampCQ8++GCXsRMnTgQAnDlzBgCg0WhsenVqa2thNBqtvT72YqqqqgDApqeojUqlQkhIiOxB5Gpllx0boi27rMeMdR+h/Ke3Qtcu16kLur6l8198sgR1QoGOp2BLqBMKfPHJkm5dl4jIHbg0EXr77beRmpqKyMiuNw48evQoAGDAAMvGlJMmTUJhYSHKy8utMdnZ2VCpVBg7dqw1JicnRza3KDs7G1qtFkOHDnXiJyHqWVp/xxLytrgZ6z7CHTn5qPvFdJz/n5tR94vpGJ+T32kSZDabcP74tzj5zb9x/vi3MJtNAIDKxu8dem9H44iI3Em3V401NDRYe2UAoLi4GAUFBQgPD8fgwYNRU1OD0tJSlJWVAQBOnToFwNI7c+0KsDNnziAnJweff/65zXvk5uYiLy8P06dPh1qtRn5+Pp599lmkpqZi8ODBAIDExETExsYiPT0dv/vd71BTU4PnnnsOGRkZ1l6ctLQ0rFy5EgsWLMDy5ctRVFSENWvW4MUXX+SKMfIoMwbGo//Zr3FJqK/MCWpHmNFfqsOMgT+2HvINCMKkX73m0PWLDuzHl1v+iIaai9ZjQeERuHPBo4gOHAi0ftflNaIDBzr0XkRE7qTbPUKHDh3CmDFjMGbMGABAZmYmxowZgxdffBEAsHPnTowZMwbJyZZ9cO677z6MGTMGb7zxhuw677zzDmJiYpCYmGjzHiqVCh9++CGmTZuG2NhYvPjii8jIyMAHH3xgjVEqlfjss8/Qr18/TJkyBfPmzcNPf/pTbNiwwRqjVquxa9cufP/99xg3bhyeeOIJZGZmIjMzs7sfm8ilfBQ++L9BEgAJEO1m/wjL7J//G6SAj6L7Wy8UHdiPnRvXyJIgAGiouYidG9fgB4PvRqhkxtXtPtsTCJXMSLprfbffm4jI1bjFRhe4xQa5E3kdIYv+qMH/DVIg7eZpHb5OCBPq6vJhMFRBpYpCaOgdkCQlzGYT/vTkwzZJ0LWC+0dAFd+IDb4nrxyx3Qv9/xlH4sFH/g5hFjAU62Cub4Ei2A+qYWpICva+ehthMqHp0GG0VlfDJzISAePGQlIqXd0s8jKOfn9z5z4iD5J28zTM+0Er9nx/BGWX9dD6h2DGwB932hNUVfUFThetgsFwdeGASqXBLcNfhKF6QKdJEADUX7qIOZPWQMr9Nf7kd0K2hD5UEshoicWDj/wdlwsvou7TszDprs7JU6r9EJpyE/zjIm7gU5Mn0Wdno3LNWrRes1DFR6NB9PJlCLEzAkDkauwR6gJ7hMiTVVV9gWOFT8J2WMuSzIT5PIG9f/iyy+v8z6L/h5FTEjqsLH258CIuvXuyw9f3f2AkkyEvoM/OxoWnnwHaf61cmZMZ8/tXmQxRr2GPEJGXE8KE00WrYH9ujwAgod78ISBFAKLz4augUMtQnJ9/EFLu/4P8SmaBuk/Pdvr6uk+/Q7/Y/hwmcwMmIZBX14CqllZE+flgYmgQlE5YPCJMJlSuWWubBAGWY5KEyjVrETxjBofJyK0wESLqoyxzguxXULcQaDVfROQtA1B9qrXDqOD+EYgZeWuH5w3FOtlwmD0mnQGGYh363RTaRaupJ31WXYfniy6g3GC0Hhug8sVvhscgOTL0hq7ddOiwbDjMhhBorahA06HDCJww/obei8iZXL7FBhH1DIOhyqG425Kmdnp++vxHoVB0/Bu8ub7zJKi7cdQzPquuwyOF52RJEABUGIx4pPAcPquuu6Hrt1ZXOzWOqLcwESLqo1SqKIfihsROQWrmcgSFy+fwBPePQGrmcgyfMLmDV1oogv0ceh9H48j5TELg+aILHQ6SAsALRRdguoEpoz4OFMbtThxRb+HQGFEfFRp6B1QqDQyGStifJyRBpdIgNPQOhE1Q4qY7JuDCyeNoqKtFUGgYYkbe2mlPUBvVMDWUar9Oh8eUahVUw9TX/2HohuTVNdj0BF1LACgzGJFX14ApYcHX9R4B48bCR6NBa2Wl/XlCkgSf6GgEjBt7Xdcn6insESLqoyRJiVuGv9j2rP1ZAMAtw1+AJFmSHYVCiUG33oaRUxIw6NbbHEqCAEBSSAhNuanTmNCUH3CitAtVtXQ8B+x64uyRlEpEL1925Um7/9dXnkcvX8aJ0uR2mAgR9WFRUUkYFfcHqFTyTYZVKg1Gxf0BUVFJTnkf/7gI9H9gJJRq+fCXUq3i0nk3EOXnWOe/o3EdCUlMRMzvX4VPu02tfaKjuXSe3BbrCHWBdYSoL+iosrTT34eVpd2SSQiMyz2BCoOxg0FSy+qx/EmxTltKz8rS5GqsI0REVpKkRFjYxJ5/H4XEJfJuSClJ+M3wGDxSeA4S5DPG2tKeXw+PcUoSBFiGybhEnjwFh8aIiDxYs9GENz8/hdV/KcCbn59Cs9FkNy45MhRvxQ2FRuUrOz5A5Yu34obecB0hIk/FobEucGiMiNzVbz84BvPXVQg2X+3JqVcIKKZG4Vc/H2X3NT1VWZrI3XBojIioD/vtB8cQ8G/boplBZgD/rsJvccxuMqSUpOteIk/UF3FojIjIwzQbTTB/bUmCpHalEdqem76u6nCYjIiuYiJERORh/rLrDILNkk0S1EaChBCzhL/sOtPLLSPyPEyEiIg8zMXqy06NI/JmTISIiDxMRKS/U+OIvBkTISIiDzN/1s2oVwgIu+URAQEBvUJg/qybe7llRJ6HiRARkYfp56uEYmoUANgkQ23PlVOj0M+X1ZyJusJEiIjIA/3q56PQlBCFhnY/xesVQFNCx3WEiEiOdYSIiDzUr34+Cs33mPCXXWdwsfoyIiL98eysm9kTRNQNTISIiDxYP18lHvufEa5uBpHH4tAYEREReS0mQkREROS1mAgRERGR12IiRERERF6LiRARERF5LSZCRERE5LWYCBEREZHXYiJEREREXosFFYlcxWwCSvYDDZVAUDQwZDKgYEVg6h3CZELTocNora6GT2QkAsaNhaTk3z/yPkyEiFzhxE4gawmgL7t6LEQLzF4PxKa6rl3kFfTZ2ahcsxatFRXWYz4aDaKXL0NIYqILW0bU+zg0RtTbTuwEtj0oT4IAQF9uOX5ip2vaRV5Bn52NC08/I0uCAKC1shIXnn4G+uxsF7WMyDWYCBH1JrPJ0hMEYefklWNZSy1xRE4mTCZUrlkLCDt//64cq1yzFsLEv3/kPZgIEfWmkv22PUEyAtBfsMQROVnTocM2PUEyQqC1ogJNhw73XqOIXIxzhIh6U0Olc+PIaUytZpTvuwBDTTNU4f0w4EcxUPr0rd8VW6urnRpH1Bd0+195Tk4OUlJSoNVqIUkSduzYITv/ySefICkpCREREZAkCQUFBTbXmDZtGiRJkj3uu+8+WUxtbS3S09OhVquhVquRnp6Ouro6WUxpaSlSUlIQGBiIiIgILFq0CC0tLbKYY8eOISEhAf7+/oiJicGqVasg7HULE/WGoGjnxpFTFH96FsXPfwNknYPqYAWQdQ7Fz3+D4k/PurppTuUTGenUOKK+oNuJUGNjI0aPHo3Nmzd3eH7KlClYt25dp9fJyMhAeXm59fHmm2/KzqelpaGgoABZWVnIyspCQUEB0tPTredNJhOSk5PR2NiIffv2YevWrfj444+xePFia4xer8esWbOg1WqRn5+PTZs2YcOGDdi4cWN3PzaRcwyZbFkdBqmDAAkIibHEUa8o/vQsfPZdgKrdL0gqIeCz70KfSoYCxo2Fj0YDSB38/ZMk+Gg0CBg3tncbRuRC3R4amzNnDubMmdPh+bZk5dy5c51eJyAgABqNxu65kydPIisrC3l5eZgwYQIA4E9/+hMmTZqEU6dOYcSIEcjOzsaJEydw/vx5aLVaAMDLL7+MBQsWYPXq1QgJCcF7772H5uZmbNmyBSqVCnFxcTh9+jQ2btyIzMxMSB39MCDqKQqlZYn8tgdhSYau/fK98vdx9robrifEGjGOMbWaYfqmDD6Azc8DSZIghIDpmzKY5gzrE8NkklKJ6OXLcOHpZyzJ0LXJ35XPH718Gf+ukFdx2b/s9957DxEREbj11lvx3HPPob6+3nouNzcXarXamgQBwMSJE6FWq7F//35rTFxcnDUJAoCkpCQYDAYcPnzYGpOQkACVSiWLKSsr6zBRMxgM0Ov1sgeRU8WmAvP+CoQMkB8P0VqO32AdIX12Ns7MmInS+fNR9txzKJ0/H2dmzOSyaDvK911AP9gmQW0kSUK/K3F9RUhiImJ+/yp8ouXDrz7R0Yj5/ausI0RexyWTpe+//34MGzYMGo0GhYWFWLZsGf7zn/9g165dAICKigpERUXZvC4qKgoVV1Y8VFRUILrdP+SwsDD4+fnJYoYOHSqLaXtNRUUFhg0bZvMea9euxcqVK2/4MxJ1KjYV+GGy0ytLt9WIab88uq1GDPhFJ2OoaYaq6zAYapp7vC29KSQxEcEzZrDXkAguSoQyMjKsf46Li8Pw4cMxbtw4HDlyBPHx8QDs/4YmhJAdv56YtonSHf0GuGzZMmRmZlqf6/V6DBo0yJGPRdQ9CiUwbKrTLtdljRhJQuWatQieMcMjvvBMZhOOVB1BdVM1IgMiER8VD6WTtyBRhfdzapwnkZRKBE4Y7+pmELmcWyyfj4+Ph6+vL4qKihAfHw+NRoPKStvlw9XV1dYeHY1GgwMHDsjO19bWwmg0ymIq2tXMqKqqAgCb3qQ2KpVKNpRG5Cm6UyPG3b8Ad5fsxrqD61DZdPXnQHRANJaOX4qZQ2Y67X0G/CgGxVnnoGr3C1QbIQQMkoRhP4px2nsSkXtxi9l/x48fh9FoxIABljkTkyZNgk6nw8GDB60xBw4cgE6nw+TJk60xhYWFKC8vt8ZkZ2dDpVJh7Nix1picnBzZkvrs7GxotVqbITMiT9dXasTsLtmNzK8yZUkQAFQ1VSHzq0zsLtnttPdS+iignGKZZ9i+rEbbc+UUrcdPlDabTTh//Fuc/ObfOH/8W5hZuZzIqts9Qg0NDThz5oz1eXFxMQoKChAeHo7BgwejpqYGpaWlKCuzVM89deoUAEvvjEajwdmzZ/Hee+/hf/7nfxAREYETJ05g8eLFGDNmDKZMmQIAGDlyJGbPno2MjAzrsvpHH30Uc+fOxYgRIwAAiYmJiI2NRXp6On73u9+hpqYGzz33HDIyMhASEgLAsgR/5cqVWLBgAZYvX46ioiKsWbMGL774IleMUZ/TF2rEmMwmrDu4DsLOFiQCAhIkrD+4HtMHTXfaMNmwlJtQDMD0TRmuHQAzSBKUU7QYlnKTU97HVYoO7MeXW/6IhpqL1mNB4RG4c8GjGD6BZRqIJNHN6oJfffUVpk+fbnN8/vz52LJlC7Zs2YKHHnrI5vyKFSvw0ksv4fz583jggQdQWFiIhoYGDBo0CMnJyVixYgXCw8Ot8TU1NVi0aBF27rRsQJmamorNmzcjNDTUGlNaWoonnngCX375Jfz9/ZGWloYNGzbIhraOHTuGJ598EgcPHkRYWBgef/zxbiVCer0earUaOp3OmmARuSNhMuHMjJloray0P09IkuATHY2b9+x22zlC+RX5+MUXv+gy7p2kd3CH5g6nvndfrCxddGA/dm5c0+H51MzlTIaoz3L0+7vbiZC3YSJEnsS6agywWyPG3ZdHf/7d51jy9ZIu49ZPXY//+cH/9EKLPJfZbMKfnnxY1hPUXnD/CDyy+W0onDwJncgdOPr97dm/7hCRjKfXiIkMcGzYztE4b3bh5PFOkyAAqL90ERdOHu+lFhG5J7dYNUZEzuPJNWLio+IRHRCNqqYqu/OEJEiIDohGfFS8C1rnWRrqap0aR9RXMREi6mGGFgM+zP0XypvrMUAViKTYSAhzLVSqKISG3gFJcn6C4qk1YpQKJZaOX4rMrzIhQZIlQ9KVLUiWjF/i9HpCTmU2Ob1Q5vUICg1zahxRX8VEiKgHvbrrfbypjEatNBTjfPPwoHkTCgsvWc+rVBrcMvxFREUlubCV7mXmkJnYOG2j3TpCS8YvcWodIac7sRPIWgLoy64eC9Fa9pe7wa1Tuitm5K0ICo/oco5QzMhbe7FVRO6Hk6W7wMnSdL1e3fU+1ilHAgDG4QCewe8AtN933vJsVNwfmAy10xuVpZ3qxM4rm+m2/5F65f+4E/aR6y6uGiNvxlVjTsJEiK6HocWA2/ftQy3CIcGM3+OXCMcldLC1J1QqDaZM/nePDJNRLzCbgFfj5D1BMpKlZ+iZY70+TGavjlBw/whMn886QtS3Ofr9zaExoh7wYe6/UCsNBQD8UJxEf1zqJFrAYChHXV0+wsIm9kr7yMlK9neSBAGAAPQXLHFO3F/OEcMnTMZNd0ywrCKrq0VQaBhiRt7KJfNEVzARIuoB5c31gJ/lz6FwbFWOwVDVgy2iHtVguzfiDcU5mUKhxKBbb3PJexO5O9YRIuoBA/oFW/9cB8dW5ahUUT3VHOppQfY3cb7uOCLqNUyEiHrAvZPmIExcAoQZ/8VIXEJ/mDuMlqBSDUBoqHO3jKBeNGSyZQ5QB7PALHOEYixxRORWmAgR9QCVnwqPmSoBSBCQ8Ff8AhJgJxmyfHHeMvwFTpR2E8aWVuR+fBhZb+Yg9+PDMLa0dv0ihdKyRB6AbTJ05fnsdS6pJ0REneOqsS701KqxC+XVePuNfQgy+qLB14iHH/8RYgZw24C+5modof4YJ/LwIN6RTZxWqQbgluEvcOm8m9jzzjc4cUgPhfnqxs1mhQGx40Iw4xdTur6A3TpCMZYkqJeXzhN5Oy6fd5KeSITWP78TP20JRaDi6m+OjWaBHX51WPIb/rDsa1xRWZq6b8873+DkwWYAV6tYA7BWtx45vp9jyZCbVJYm8nZMhJzE2YnQ+ud3Is0YCgCQpGt+2F753/C+L5Mhot5mbGnFG8/sgmT2kyVBbQQEhKIFj786C75+XGxL5Am4+7wbulBejZ+2hAKQJ0HXPv9pSygulFf3dtOIvNqhT/8DhVllNwkCLD1ECrMKhz79Ty+3jIh6GhOhXvT2G/sQqJBskqA2kiQhUCHh7Tf29XLLiLyb7mKjU+OIyHMwEepFQUZfp8YRkXOoIwKdGkdEnoOJUC9q8DU6NY6InGNcymiYFQbrxOj2BATMCgPGpYzu5ZYRUU9jItSLHn78R2g0C3Q0P10IgUazwMOP/6iXW0buzmQ2Ib8iH59/9znyK/JhMptc3aQ+xdfPB7HjLJMp2ydDbc9jx4Vc90RpkxD4prYe2ytr8U1tPUxco0LkNrj8oRfFDIjEer9cpBlDIYSwu2psh18dlrCeEF1jd8lurDu4DpVNV/epig6IxtLxSzFzyEwXtqxvsSyNt9QRkq6pIyQULY7XEbLjs+o6PF90AeWGqz29A1S++M3wGCRHht5gq4noRnH5fBdYR4hcaXfxF3j/i6cQYWpFtVKJI/1UMEuSdXXTxmkbmQw5mbGlFYc+/Q90FxuhjgjEuJTR190T9Fl1HR4pPGcz4Nb2L/+tuKFMhoh6COsIOQkrS5OrmI7vwKXtjyCq9WpPQoVSiXX9w7AnMAASJEQHRCPr7iwoWbDP7ZiEwLjcE7KeoGtJsPQM5U+KhbKDlaREdP0c/f7m0JiLxAyIxIsr/9fVzSB3dWInFH+fj4h2h6NMJmysuojMqAjsCQxARVMFjlQdwR0a79iwVZgFDMU6mOtboAj2g2qYGpLCPZOIvLqGDpMgABAAygxG5NU1YEpYcO81jIhkmAgRuRuzybJfFWxXMyhg2bh1yaVa7A3wh1mSUN3kHQU4LxdeRN2nZ2HStViPKdV+CE25Cf5x7VNG16tyZLPWbsQRUc/gqjEid1OyH9CXdVDj2PKPdoDJhPhmAwAgMqDvD6leLryIS++elCVBAGDSteDSuydxufCii1rWsSgH5xU5GkdEPYOJEJG7aajsOgZAlMkMTYAG8VHxPdwg1xJmgbpPz3YaU/fpdxBm95ruODE0CANUvh0mtBIArcoXE0ODerNZRNQOEyEidxMU7VBYtVKJJeOX9PmJ0oZinU1PUHsmnQGGYl0vtcgxSknCb4bHAIBNMtT2/NfDYzhRmsjFmAgRuZshk4EQLWy/Pi3MAKp8/JCWtMkrls6b6ztPgrob15uSI0PxVtxQaFTybXMGqHy5dJ7ITXBwmsjdKJTA7PXAtgdhSYauDvmIK0f6/++fMHNYkosa2LsUwX5OjettyZGhmB2hRl5dA6paWhHl54OJoUHsCSJyE+wRInJHsanAvL8CIQNkh6WQGEjz/gblrT91TbtcQDVMDaW68yRHqVZBNUzdSy3qPqUkYUpYMP43OgxTwoKZBBG5EfYIEbmr2FTgh8mWVWQNlZa5Q0MmW3qMvIikkBCachMuvXuyw5jQlB+4bT0hInJvTISI3JlCCQyb6upWuJx/XAT6PzDSTh0hFUJTfuCWdYSIyDMwESIij+AfF4F+sf09prI0EXkGJkJE5DEkhYR+N4W6uhlE1IdwsjQRERF5LSZCRERE5LWYCBEREZHXYiJEREREXqvbiVBOTg5SUlKg1WohSRJ27NghO//JJ58gKSkJERERkCQJBQUFsvM1NTV46qmnMGLECAQEBGDw4MFYtGgRdDr5PkFDhw6FJEmyx9KlS2UxpaWlSElJQWBgICIiIrBo0SK0tMjL7B87dgwJCQnw9/dHTEwMVq1aBSHca3NGIiIico1urxprbGzE6NGj8dBDD+Huu++2e37KlCn42c9+hoyMDJvzZWVlKCsrw4YNGxAbG4uSkhI8/vjjKCsrw0cffSSLXbVqlewaQUFXd2k2mUxITk5GZGQk9u3bh0uXLmH+/PkQQmDTpk0AAL1ej1mzZmH69OnIz8/H6dOnsWDBAgQGBmLx4sXd/ehERETU14gbAEBs377d7rni4mIBQBw9erTL62zbtk34+fkJo9FoPTZkyBDxyiuvdPiazz//XCgUCnHhwgXrsQ8++ECoVCqh0+mEEEK89tprQq1Wi+bmZmvM2rVrhVarFWazuct2CSGETqcTAKzXJCIiIvfn6Pe3W8wR0ul0CAkJgY+PvINq/fr16N+/P26//XasXr1aNuyVm5uLuLg4aLVa67GkpCQYDAYcPnzYGpOQkACVSiWLKSsrw7lz5+y2xWAwQK/Xyx5ERETUN7m8oOKlS5fw61//Go899pjs+NNPP434+HiEhYXh4MGDWLZsGYqLi/HWW28BACoqKhAdHS17TVhYGPz8/FBRUWGNGTp0qCym7TUVFRUYNmyYTXvWrl2LlStXOuvjERERkRtzaSKk1+uRnJyM2NhYrFixQnbu2Weftf75tttuQ1hYGO655x5rLxEASHZ2cBZCyI63jxFXJkrbey0ALFu2DJmZmbI2Dho0qJufjIiIiDyBy4bG6uvrMXv2bAQFBWH79u3w9fXtNH7ixIkAgDNnzgAANBqNteenTW1tLYxGo7XXx15MVVUVANj0JrVRqVQICQmRPYiIiKhvckkipNfrkZiYCD8/P+zcuRP9+vXr8jVHjx4FAAwYMAAAMGnSJBQWFqK8vNwak52dDZVKhbFjx1pjcnJyZHOLsrOzodVqbYbMiIiIyPt0e2isoaHB2isDAMXFxSgoKEB4eDgGDx6MmpoalJaWoqysDABw6tQpAJbeGY1Gg/r6eiQmJqKpqQnvvvuubEJyZGQklEolcnNzkZeXh+nTp0OtViM/Px/PPvssUlNTMXjwYABAYmIiYmNjkZ6ejt/97neoqanBc889h4yMDGsvTlpaGlauXIkFCxZg+fLlKCoqwpo1a/Diiy92ODRGREREXqS7y9H27t0rANg85s+fL4QQ4s9//rPd8ytWrOj09QBEcXGxEEKIw4cPiwkTJgi1Wi369esnRowYIVasWCEaGxtlbSkpKRHJycnC399fhIeHi4ULF8qWygshxLfffiumTp0qVCqV0Gg04qWXXnJ46bwQXD5PRETkiRz9/paEYJnlzuj1eqjVausSfyIiInJ/jn5/u0UdISIiIiJXYCJEREREXouJEBEREXktJkJERETktZgIERERkddiIkRERERei4kQEREReS0mQkREROS1mAgRERGR12IiRERERF6LiRARERF5LSZCRERE5LWYCBEREZHXYiJEREREXouJEBEREXktJkJERETktZgIERERkddiIkRERERei4kQEREReS0mQkREROS1mAgRERGR12IiRERERF6LiRARERF5LSZCRERE5LWYCBEREZHXYiJEREREXouJEBEREXktJkJERETktZgIERERkddiIkRERERei4kQEREReS0mQkREROS1mAgRERGR12IiRERERF6LiRARERF5LSZCRERE5LWYCBEREZHX6nYilJOTg5SUFGi1WkiShB07dsjOf/LJJ0hKSkJERAQkSUJBQYHNNQwGA5566ilEREQgMDAQqamp+P7772UxtbW1SE9Ph1qthlqtRnp6Ourq6mQxpaWlSElJQWBgICIiIrBo0SK0tLTIYo4dO4aEhAT4+/sjJiYGq1atghCiux+biIiI+qBuJ0KNjY0YPXo0Nm/e3OH5KVOmYN26dR1e45lnnsH27duxdetW7Nu3Dw0NDZg7dy5MJpM1Ji0tDQUFBcjKykJWVhYKCgqQnp5uPW8ymZCcnIzGxkbs27cPW7duxccff4zFixdbY/R6PWbNmgWtVov8/Hxs2rQJGzZswMaNG7v7sYmIiKgvEjcAgNi+fbvdc8XFxQKAOHr0qOx4XV2d8PX1FVu3brUeu3DhglAoFCIrK0sIIcSJEycEAJGXl2eNyc3NFQDEf//7XyGEEJ9//rlQKBTiwoUL1pgPPvhAqFQqodPphBBCvPbaa0KtVovm5mZrzNq1a4VWqxVms9mhz6jT6QQA6zWJiIjI/Tn6/d3rc4QOHz4Mo9GIxMRE6zGtVou4uDjs378fAJCbmwu1Wo0JEyZYYyZOnAi1Wi2LiYuLg1artcYkJSXBYDDg8OHD1piEhASoVCpZTFlZGc6dO2e3fQaDAXq9XvYgIiKivqnXE6GKigr4+fkhLCxMdjw6OhoVFRXWmKioKJvXRkVFyWKio6Nl58PCwuDn59dpTNvztpj21q5da52XpFarMWjQoOv4lEREROQJ3GbVmBACkiRZn1/7Z2fGiCsTpe29FgCWLVsGnU5nfZw/f757H4SIiIg8Rq8nQhqNBi0tLaitrZUdr6qqsvbWaDQaVFZW2ry2urpaFtO+V6e2thZGo7HTmKqqKgCw6Slqo1KpEBISInsQERFR39TridDYsWPh6+uLXbt2WY+Vl5ejsLAQkydPBgBMmjQJOp0OBw8etMYcOHAAOp1OFlNYWIjy8nJrTHZ2NlQqFcaOHWuNycnJkS2pz87OhlarxdChQ3vyYxIREZEH6HYi1NDQgIKCAmt9oOLiYhQUFKC0tBQAUFNTg4KCApw4cQIAcOrUKRQUFFh7ZtRqNR5++GEsXrwYe/bswdGjR/HAAw9g1KhRmDlzJgBg5MiRmD17NjIyMpCXl4e8vDxkZGRg7ty5GDFiBAAgMTERsbGxSE9Px9GjR7Fnzx4899xzyMjIsPbipKWlQaVSYcGCBSgsLMT27duxZs0aZGZmdjg0RkRERF6ku8vR9u7dKwDYPObPny+EEOLPf/6z3fMrVqywXuPy5cti4cKFIjw8XPj7+4u5c+eK0tJS2ftcunRJ3H///SI4OFgEBweL+++/X9TW1spiSkpKRHJysvD39xfh4eFi4cKFsqXyQgjx7bffiqlTpwqVSiU0Go146aWXHF46LwSXzxMREXkiR7+/JSFYZrkzer0earUaOp2O84WIiIg8hKPf326zaoyIiIiotzERIiIiIq/FRIiIiIi8FhMhIiIi8lpMhIiIiMhrMREiIiIir8VEiIiIiLwWEyEiIiLyWkyEiIiIyGsxESIiIiKvxUSIiIiIvBYTISIiIvJaTISIiIjIazERIiIiIq/FRIiIiIi8FhMhIiIi8lpMhIiIiMhrMREiIiIir8VEiIiIiLwWEyEiIiLyWkyEiIiIyGsxESIiIiKvxUSIiIiIvBYTISIiIvJaTISIiIjIazERIiIiIq/FRIiIiIi8FhMhIiIi8lpMhIiIiMhrMREiIiIir8VEiIiIiLyWj6sbQEREjjOZTThSdQTVTdWIDIhEfFQ8lAqlq5tF5LGYCBEReYjdJbux7uA6VDZVWo9FB0Rj6filmDlkpgtbRuS5ODRGROQBdpfsRuZXmbIkCACqmqqQ+VUmdpfsdlHLiDwbEyEiIjdnMpuw7uA6CAibc23H1h9cD5PZ1NtNI/J4TISIiNzckaojNj1B1xIQqGiqwJGqI73YKqK+gYkQEZGbq26qdmocEV3FRIiIyM1FBkQ6NY6Irup2IpSTk4OUlBRotVpIkoQdO3bIzgsh8NJLL0Gr1cLf3x/Tpk3D8ePHrefPnTsHSZLsPv7+979b44YOHWpzfunSpbL3Ki0tRUpKCgIDAxEREYFFixahpaVFFnPs2DEkJCTA398fMTExWLVqFYSwHWcnInJX8VHxiA6IhgTJ7nkJEjQBGsRHxfdyy4g8X7cTocbGRowePRqbN2+2e/63v/0tNm7ciM2bNyM/Px8ajQazZs1CfX09AGDQoEEoLy+XPVauXInAwEDMmTNHdq1Vq1bJ4p5//nnrOZPJhOTkZDQ2NmLfvn3YunUrPv74YyxevNgao9frMWvWLGi1WuTn52PTpk3YsGEDNm7c2N2PTUTkMkqFEkvHW34RbJ8MtT1fMn4J6wkRXQ9xAwCI7du3W5+bzWah0WjEunXrrMeam5uFWq0Wb7zxRofXuf3228UvfvEL2bEhQ4aIV155pcPXfP7550KhUIgLFy5Yj33wwQdCpVIJnU4nhBDitddeE2q1WjQ3N1tj1q5dK7RarTCbzQ59Rp1OJwBYr0lE5Cq7zu0SM7bNEHFb4qyPmdtmil3ndrm6aURux9Hvb6fOESouLkZFRQUSExOtx1QqFRISErB//367rzl8+DAKCgrw8MMP25xbv349+vfvj9tvvx2rV6+WDXvl5uYiLi4OWq3WeiwpKQkGgwGHDx+2xiQkJEClUsliysrKcO7cObvtMRgM0Ov1sgcRkTuYOWQmvrj7C7yT9A7WT12Pd5LeQdbdWSymSHQDnFpZuqKiAgAQHR0tOx4dHY2SkhK7r3n77bcxcuRITJ48WXb86aefRnx8PMLCwnDw4EEsW7YMxcXFeOutt6zv1f59wsLC4OfnZ21HRUUFhg4datOWtnPDhg2zac/atWuxcuVKBz8xEVHvUiqUuENzh6ubQdRn9MgWG5IkH8MWQtgcA4DLly/j/fffxwsvvGBz7tlnn7X++bbbbkNYWBjuueceay+Rvfex91722tLRawFg2bJlyMzMtD7X6/UYNGiQ3VgiIiLybE4dGtNoNACu9gy1qaqqsum9AYCPPvoITU1NePDBB7u89sSJEwEAZ86csb5X+/epra2F0Wi0vpe9mKqqKgC2vVZtVCoVQkJCZA8iIiLqm5yaCA0bNgwajQa7du2yHmtpacG///1vm6EvwDIslpqaisjIrmtfHD16FAAwYMAAAMCkSZNQWFiI8vJya0x2djZUKhXGjh1rjcnJyZHNLcrOzoZWq7UZMiMiIiLv0+1EqKGhAQUFBSgoKABgmSBdUFCA0tJSSJKEZ555BmvWrMH27dtRWFiIBQsWICAgAGlpabLrnDlzBjk5OXjkkUds3iM3NxevvPIKCgoKUFxcjG3btuGxxx5DamoqBg8eDABITExEbGws0tPTcfToUezZswfPPfccMjIyrL04aWlpUKlUWLBgAQoLC7F9+3asWbMGmZmZHQ6NERERkRfp7nK0vXv3CgA2j/nz5wshLEvoV6xYITQajVCpVOLHP/6xOHbsmM11li1bJgYOHChMJpPNucOHD4sJEyYItVot+vXrJ0aMGCFWrFghGhsbZXElJSUiOTlZ+Pv7i/DwcLFw4ULZUnkhhPj222/F1KlThUqlEhqNRrz00ksOL50XgsvniYiIPJGj39+SECyz3Bm9Xg+1Wg2dTsf5QkRERB7C0e9v7jVGREREXouJEBEREXktJkJERETktXqkoGJf0jaFilttEBEReY627+2upkIzEepCfX09ALC6NBERkQeqr6+HWq3u8DxXjXXBbDajrKwMwcHBvVJ7qG1Lj/Pnz3OV2g3gfbxxvIfOwft443gPb5w33kMhBOrr66HVaqFQdDwTiD1CXVAoFBg4cGCvvy+393AO3scbx3voHLyPN4738MZ52z3srCeoDSdLExERkddiIkRERERei4mQm1GpVFixYgVUKpWrm+LReB9vHO+hc/A+3jjewxvHe9gxTpYmIiIir8UeISIiIvJaTISIiIjIazERIiIiIq/FRIiIiIi8FhOhbsrJyUFKSgq0Wi0kScKOHTtk5z/55BMkJSUhIiICkiShoKCgw2sJITBnzhy716mtrUV6ejrUajXUajXS09NRV1cniyktLUVKSgoCAwMRERGBRYsWoaWlRRZz7NgxJCQkwN/fHzExMVi1alWX+670Bmfdx9zcXNx5550IDAxEaGgopk2bhsuXL1vP9+X76Ix7WFFRgfT0dGg0GgQGBiI+Ph4fffSRLMZb76HRaMSSJUswatQoBAYGQqvV4sEHH0RZWZnsGgaDAU899RQiIiIQGBiI1NRUfP/997KYvnwPgRu/jzU1NXjqqacwYsQIBAQEYPDgwVi0aBF0Op3sffryfXTG38U23vzdcj2YCHVTY2MjRo8ejc2bN3d4fsqUKVi3bl2X13r11Vc73LYjLS0NBQUFyMrKQlZWFgoKCpCenm49bzKZkJycjMbGRuzbtw9bt27Fxx9/jMWLF1tj9Ho9Zs2aBa1Wi/z8fGzatAkbNmzAxo0bu/mpnc8Z9zE3NxezZ89GYmIiDh48iPz8fCxcuFBWSr0v30dn3MP09HScOnUKO3fuxLFjx3DXXXfh3nvvxdGjR60x3noPm5qacOTIEbzwwgs4cuQIPvnkE5w+fRqpqamyuGeeeQbbt2/H1q1bsW/fPjQ0NGDu3LkwmUzWmL58D4Ebv49lZWUoKyvDhg0bcOzYMWzZsgVZWVl4+OGHZdfqy/fRGX8X23jzd8t1EXTdAIjt27fbPVdcXCwAiKNHj9o9X1BQIAYOHCjKy8ttrnPixAkBQOTl5VmP5ebmCgDiv//9rxBCiM8//1woFApx4cIFa8wHH3wgVCqV0Ol0QgghXnvtNaFWq0Vzc7M1Zu3atUKr1Qqz2Xydn9r5rvc+TpgwQTz//PMdXteb7uP13sPAwEDx17/+VXYsPDxcvPXWW0II3sP2Dh48KACIkpISIYQQdXV1wtfXV2zdutUac+HCBaFQKERWVpYQwrvuoRDXdx/t2bZtm/Dz8xNGo1EI4V338UbuIb9buo89Qi7Q1NSEn//859i8eTM0Go3N+dzcXKjVakyYMMF6bOLEiVCr1di/f781Ji4uDlqt1hqTlJQEg8GAw4cPW2MSEhJkBbSSkpJQVlaGc+fO9dCn6x1VVVU4cOAAoqKiMHnyZERHRyMhIQH79u2zxvA+du1HP/oRPvzwQ9TU1MBsNmPr1q0wGAyYNm0aAN7D9nQ6HSRJQmhoKADg8OHDMBqNSExMtMZotVrExcXJ7g/voVz7+9hRTEhICHx8LFti8j7K2buH/G65PkyEXODZZ5/F5MmT8ZOf/MTu+YqKCkRFRdkcj4qKQkVFhTUmOjpadj4sLAx+fn6dxrQ9b4vxVN999x0A4KWXXkJGRgaysrIQHx+PGTNmoKioCADvoyM+/PBDtLa2on///lCpVHjsscewfft23HTTTQB4D6/V3NyMpUuXIi0tzbppZUVFBfz8/BAWFiaLjY6Oln123sOr7N3H9i5duoRf//rXeOyxx6zHeB+v6uge8rvl+nD3+V62c+dOfPnll7I5GPbYG98VQsiOX0+MuDKZraPxY09hNpsBAI899hgeeughAMCYMWOwZ88evPPOO1i7di0A3seuPP/886itrcXu3bsRERGBHTt24Gc/+xm+/vprjBo1CgDvIWCZrHrffffBbDbjtdde6zLeGffHkRhPuoeAY/dRr9cjOTkZsbGxWLFihewc72PH95DfLdePPUK97Msvv8TZs2cRGhoKHx8fa7fv3XffbR2O0Gg0qKystHltdXW1NevWaDQ2mXdtbS2MRmOnMVVVVQBgk817mgEDBgAAYmNjZcdHjhyJ0tJSALyPXTl79iw2b96Md955BzNmzMDo0aOxYsUKjBs3Dn/4wx8A8B4Cli+eefPmobi4GLt27ZL9Bq7RaNDS0oLa2lrZa6qqqmSf3dvvIdD5fWxTX1+P2bNnIygoCNu3b4evr6/1HO9j5/eQ3y3Xj4lQL1u6dCm+/fZbFBQUWB8A8Morr+DPf/4zAGDSpEnQ6XQ4ePCg9XUHDhyATqfD5MmTrTGFhYUoLy+3xmRnZ0OlUmHs2LHWmJycHNmyx+zsbGi1WgwdOrSHP2nPGjp0KLRaLU6dOiU7fvr0aQwZMgQA72NXmpqaAEC2yg4AlEqltcfN2+9h2xdPUVERdu/ejf79+8vOjx07Fr6+vti1a5f1WHl5OQoLC2X3x5vvIdD1fQQsPUGJiYnw8/PDzp070a9fP9l5b7+PXd1DfrfcgF6enO3x6uvrxdGjR8XRo0cFALFx40Zx9OhR68z9S5cuiaNHj4rPPvtMABBbt24VR48eFeXl5R1eE3ZWCMyePVvcdtttIjc3V+Tm5opRo0aJuXPnWs+3traKuLg4MWPGDHHkyBGxe/duMXDgQLFw4UJrTF1dnYiOjhY///nPxbFjx8Qnn3wiQkJCxIYNG5x7U66DM+7jK6+8IkJCQsTf//53UVRUJJ5//nnRr18/cebMGWtMX76PN3oPW1paxM033yymTp0qDhw4IM6cOSM2bNggJEkSn332mfV9vPUeGo1GkZqaKgYOHCgKCgpEeXm59WEwGKzXePzxx8XAgQPF7t27xZEjR8Sdd94pRo8eLVpbW60xffkeCnHj91Gv14sJEyaIUaNGiTNnzshivOU+OuPvYnve+N1yPZgIddPevXsFAJvH/PnzhRBC/PnPf7Z7fsWKFR1e095f1kuXLon7779fBAcHi+DgYHH//feL2tpaWUxJSYlITk4W/v7+Ijw8XCxcuFC2nFEIIb799lsxdepUoVKphEajES+99JJbLG901n1cu3atGDhwoAgICBCTJk0SX3/9tex8X76PzriHp0+fFnfddZeIiooSAQEB4rbbbrNZTu+t97Ct7IC9x969e63XuHz5sli4cKEIDw8X/v7+Yu7cuaK0tFT2Pn35Hgpx4/exo9cDEMXFxdb36cv30Rl/F9vzxu+W6yEJ4aGlIImIiIhuEOcIERERkddiIkRERERei4kQEREReS0mQkREROS1mAgRERGR12IiRERERF6LiRARERF5LSZCRERE5LWYCBEREZHXYiJEREREXouJEBEREXktJkJERETktf5/lKBVBoZIGUoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for t in AthensVotingList:\n",
    "    plt.scatter(snappedHDpop[t], snappedHD3pop[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "43d19d97-62ca-4aa7-a46d-6b9b7fd1c114",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4-30-23 alternate voting - assign vote weight directly from Athens plans\n",
      "Voting done.  The most popular district was 1 with 0.149 of the vote\n",
      "Voting done.  The most popular district was 1 with 0.131 of the vote\n"
     ]
    }
   ],
   "source": [
    "print(\"4-30-23 alternate voting - assign vote weight directly from Athens plans\")\n",
    "planLists = [AthensLISTS, AthensLISTS3]\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "regionalVotingList = AthensVotingList.copy()\n",
    "reglVTDwts = set1DplanWeights(regionalVotingList, planLists[0], tractCP, tractPop3)\n",
    "reglMuniWts = set1DplanWeights(regionalVotingList, planLists[1], tractCP, tractPop3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "8c0967f0-1adf-4353-9ef3-442377b82606",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving Athens vtd and muni lists with voting to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Saving Athens vtd and muni lists with voting to a file\")\n",
    "#currentRegionNo = 7 #SE\n",
    "date = \"30Apr\"\n",
    "rLISTS = [AthensLISTS.copy(), AthensLISTS3.copy()]  \n",
    "rWeights = [reglVTDwts.copy(), reglMuniWts.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, voteFrac = list(), list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "        voteFrac.append(rWeights[i][j])\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":voteFrac,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"Athens\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "26915c66-3c9c-4c82-97a8-3df18f84d35a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "115282.8686868687 2055.842424242437 are target and tolrPop based on countyPop/aDP = 1.918\n",
      "failed contiguity for muniSnap for starter tract, angle 1765 306.659\n",
      "failed contiguity for muniSnap for starter tract, angle 1766 293.216\n",
      "failed contiguity for muniSnap for starter tract, angle 1767 278.122\n",
      "      t 1768 3.112 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1769 3.604 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1770 294.241\n",
      "failed contiguity for muniSnap for starter tract, angle 1771 352.801\n",
      "failed contiguity for muniSnap for starter tract, angle 1772 319.126\n",
      "failed contiguity for muniSnap for starter tract, angle 1773 326.688\n",
      "failed contiguity for muniSnap for starter tract, angle 1774 266.93\n",
      "      t 1775 61.656 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfListB for tract 1776 PUNT! for this tract\n",
      "no 14 county neighbors to halfListB for tract 1777 PUNT! for this tract\n",
      "      t 1778 245.022 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1779 256.524 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1780 249.89 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1781 214.048 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1782 262.118\n",
      "      t 1783 107.096 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1784 10.841 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1785 343.215 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1786 333.136\n",
      "failed contiguity for muniSnap for starter tract, angle 1787 356.853\n",
      "failed contiguity for muniSnap for starter tract, angle 1788 16.091\n",
      "failed contiguity for vtdSnap for starter tract, angle 1789 25.727\n",
      "no 14 county neighbors to halfListB for tract 1790 PUNT! for this tract\n",
      "failed contiguity for muniSnap for starter tract, angle 1791 268.492\n",
      "failed contiguity for muniSnap for starter tract, angle 1792 282.933\n",
      "      t 1793 238.982 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfListB for tract 1794 PUNT! for this tract\n",
      "      t 1795 253.473 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1796 290.092\n",
      "      t 1797 334.541 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1798 332.419\n",
      "failed contiguity for muniSnap for starter tract, angle 1799 324.014\n",
      "failed contiguity for muniSnap for starter tract, angle 1800 314.107\n",
      "failed contiguity for muniSnap for starter tract, angle 1801 301.977\n",
      "no 14 county neighbors to halfList3B for tract 1802 PUNT! for this tract\n",
      "      t 1803 237.307 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1804 224.298 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1805 104.625 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1806 268.835\n",
      "failed contiguity for muniSnap for starter tract, angle 1809 266.456\n",
      "no 14 county neighbors to halfList3B for tract 1810 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 1811 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 1812 PUNT! for this tract\n",
      "      t 1813 212.571 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfListB for tract 1814 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 1815 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 1816 PUNT! for this tract\n",
      "      t 1817 227.213 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1818 235.533 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1819 291.048\n",
      "failed contiguity for muniSnap for starter tract, angle 1820 287.883\n",
      "failed contiguity for muniSnap for starter tract, angle 1821 280.388\n",
      "failed contiguity for muniSnap for starter tract, angle 1822 282.695\n",
      "      t 1823 124.008 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1824 110.403 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfListB for tract 1825 PUNT! for this tract\n",
      "      t 1826 102.226 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1827 91.805 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1828 111.167 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1829 115.155 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfListB for tract 1830 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 1831 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 1832 PUNT! for this tract\n",
      "      t 1833 247.895 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1834 245.708 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1835 262.137\n",
      "      t 1836 107.321 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1837 223.494 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1838 265.205\n",
      "      t 1839 239.61 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1840 282.248\n",
      "      t 1841 112.81 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1842 275.58\n",
      "failed contiguity for muniSnap for starter tract, angle 1843 262.63\n",
      "      t 1844 245.319 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1845 256.769 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1846 10.05 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1847 90.873 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1848 81.152 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1849 101.663 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1850 111.018 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 1851 PUNT! for this tract\n",
      "failed contiguity for muniSnap for starter tract, angle 1874 258.788\n",
      "      t 1875 252.704 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1876 241.795 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1877 271.273\n",
      "      t 1878 243.845 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1879 235.369 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1880 300.376\n",
      "failed contiguity for muniSnap for starter tract, angle 1881 298.917\n",
      "failed contiguity for muniSnap for starter tract, angle 1882 355.796\n",
      "failed contiguity for muniSnap for starter tract, angle 1883 284.93\n",
      "failed contiguity for muniSnap for starter tract, angle 1884 284.186\n",
      "failed contiguity for muniSnap for starter tract, angle 1885 273.963\n",
      "failed contiguity for muniSnap for starter tract, angle 1886 306.861\n",
      "1887 347.126 t, deg, FAILED TO meet district pops in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1888 333.455\n",
      "failed contiguity for muniSnap for starter tract, angle 1889 353.39\n",
      "failed contiguity for muniSnap for starter tract, angle 1890 327.923\n",
      "1891 347.107 t, deg, FAILED TO meet district pops in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 1892 319.218\n",
      "failed contiguity for muniSnap for starter tract, angle 1893 303.702\n",
      "      t 1894 34.024 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfListB for tract 1895 PUNT! for this tract\n",
      "      t 1896 203.28 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1897 211.494 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 1898 112.205 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 1899 PUNT! for this tract\n",
      "      t 2124 39.03 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for vtdSnap for starter tract, angle 2125 23.101\n",
      "      t 2126 46.808 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 2127 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 2128 PUNT! for this tract\n",
      "      t 2129 72.635 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2130 79.055 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 2131 PUNT! for this tract\n",
      "      t 2132 35.685 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2133 44.09 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for vtdSnap for starter tract, angle 2134 26.746\n",
      "      t 2135 20.651 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 2136 PUNT! for this tract\n",
      "failed contiguity for vtdSnap for starter tract, angle 2137 24.553\n",
      "      t 2138 45.397 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2145 37.302 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2146 35.438 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for vtdSnap for starter tract, angle 2147 55.878\n",
      "      t 2148 44.797 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2149 28.422 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2150 61.722 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2151 56.332\n",
      "      t 2152 70.605 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 2153 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 2154 PUNT! for this tract\n",
      "      t 2155 43.836 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2156 67.292 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 2157 PUNT! for this tract\n",
      "      t 2158 164.53 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2159 159.638 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2160 224.033 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2161 250.755 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2162 290.478\n",
      "failed contiguity for muniSnap for starter tract, angle 2163 282.999\n",
      "failed contiguity for muniSnap for starter tract, angle 2164 285.246\n",
      "failed contiguity for muniSnap for starter tract, angle 2165 286.55\n",
      "failed contiguity for muniSnap for starter tract, angle 2166 281.312\n",
      "      t 2167 101.344 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2168 122.585 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2169 280.233\n",
      "failed contiguity for muniSnap for starter tract, angle 2170 292.668\n",
      "      t 2171 104.67 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2172 117.033 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2173 122.657 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfListB for tract 2174 PUNT! for this tract\n",
      "no 14 county neighbors to halfList3B for tract 2175 PUNT! for this tract\n",
      "      t 2176 82.94 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2177 256.617 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2178 264.957\n",
      "      t 2179 102.59 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2180 109.878 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2181 110.671 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2182 113.239 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2183 230.306 is a good deg Angle for creating 2 districts in county 49\n",
      "no 14 county neighbors to halfList3B for tract 2184 PUNT! for this tract\n",
      "      t 2185 257.445 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2186 213.357 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2187 272.723\n",
      "failed contiguity for muniSnap for starter tract, angle 2188 267.638\n",
      "failed contiguity for muniSnap for starter tract, angle 2189 274.165\n",
      "failed contiguity for muniSnap for starter tract, angle 2190 276.329\n",
      "failed contiguity for muniSnap for starter tract, angle 2191 268.502\n",
      "failed contiguity for muniSnap for starter tract, angle 2192 276.048\n",
      "failed contiguity for muniSnap for starter tract, angle 2193 270.257\n",
      "failed contiguity for muniSnap for starter tract, angle 2194 265.061\n",
      "failed contiguity for muniSnap for starter tract, angle 2195 274.28\n",
      "failed contiguity for muniSnap for starter tract, angle 2196 271.479\n",
      "failed contiguity for muniSnap for starter tract, angle 2197 270.345\n",
      "failed contiguity for muniSnap for starter tract, angle 2198 306.269\n",
      "failed contiguity for muniSnap for starter tract, angle 2199 299.539\n",
      "failed contiguity for muniSnap for starter tract, angle 2200 295.338\n",
      "failed contiguity for muniSnap for starter tract, angle 2201 289.895\n",
      "failed contiguity for muniSnap for starter tract, angle 2202 287.425\n",
      "failed contiguity for muniSnap for starter tract, angle 2203 313.389\n",
      "failed contiguity for muniSnap for starter tract, angle 2204 316.931\n",
      "failed contiguity for muniSnap for starter tract, angle 2205 312.107\n",
      "      t 2206 91.136 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2207 99.368 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2208 267.123\n",
      "      t 2209 237.263 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2210 250.615 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2211 110.051 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2212 286.783\n",
      "failed contiguity for muniSnap for starter tract, angle 2213 267.829\n",
      "      t 2214 209.11 is a good deg Angle for creating 2 districts in county 49\n",
      "2215 202.991 t, deg, FAILED TO meet district pops in county 49\n",
      "      t 2216 219.94 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2217 85.056 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2218 96.678 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2219 117.417 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2220 85.154 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2221 261.177\n",
      "      t 2222 253.515 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2223 246.915 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 2224 238.936 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2225 264.253\n",
      "      t 2226 227.705 is a good deg Angle for creating 2 districts in county 49\n",
      "failed contiguity for muniSnap for starter tract, angle 2227 305.24\n",
      "failed contiguity for muniSnap for starter tract, angle 2228 315.475\n",
      "      t 5876 110.559 is a good deg Angle for creating 2 districts in county 49\n",
      "      t 7606 221.078 is a good deg Angle for creating 2 districts in county 49\n",
      "we created 97 good 2-district plans out of 212 tries in county 49\n"
     ]
    }
   ],
   "source": [
    "#3/13/23 Mahoning - unlike Lake, each tract defines its own sliceAngle, and we spill into Columbiana if needed\n",
    "#3/13/23  --this works; we create 90+ possible 2-district plans, some of which need a little Columbiana.  OK that >50% fail\n",
    "mC = 49 #Mahoning\n",
    "mCTL = countyTractList[mC]  \n",
    "ppC = 14 #Columbiana\n",
    "aDP = avgDistrictPop\n",
    "shade = 0.8\n",
    "targPop = shade*countyPop[mC] / round(countyPop[mC]/avgDistrictPop,0) + (1.-shade)*aDP\n",
    "tolrPop = min(1.05 * avgDistrictPop - targPop, targPop - 0.95*avgDistrictPop)\n",
    "print(targPop, tolrPop,\"are target and tolrPop based on countyPop/aDP =\",r3(countyPop[mC]/avgDistrictPop) )\n",
    "\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "LISTS, LISTS3, goodMahoningAngles, goodMahoningStarterTracts = list(), list(), list(), list()\n",
    "cutPoint = countyPCP[mC]\n",
    "nCuts = len(mCTL)  #15\n",
    "#minAngle,maxAngle = 0.4*math.pi, 0.95 * math.pi\n",
    "deebug = False\n",
    "for t in mCTL:\n",
    "    sliceAngle = getSliceAngle(tractCP[t], cutPoint, tractCP[t])\n",
    "    halfList, halfPop = cutVtdSnap(targPop, tolrPop, tractCP, mCTL, neighborList, tractPop, sliceAngle, cutPoint)\n",
    "    halfList3, hML, halfPop3, splitMuni = cutMuniSnap(targPop, tolrPop, muniTractList,tractCP, mCTL,\n",
    "                                                  muniNeighborList,tractPop3, muniPop, muniPCP, sliceAngle, cutPoint, DEBUG=\"no\")\n",
    "    popGap = targPop - halfPop3\n",
    "    if popGap > tolrPop:  #needed to split a muni, but didn't\n",
    "        list3B, pop3B, nSplits = tryNoSplit(t, popGap, tolrPop, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,hML,\n",
    "                                                  countyMuniList[mC],tractCP, neighborList,tractPop3,0)        \n",
    "        halfList3, halfPop3 = halfList3 + list3B, halfPop3 + pop3B\n",
    "\n",
    "    #print(\"our final halfPop vs aDP is\",halfPop, targPop,'for angle (deg) =',r3(sliceAngle*180./math.pi))\n",
    "    if deebug:\n",
    "        plotPoly(tractCP[t].buffer(0.02))\n",
    "        for v in halfList:\n",
    "            plotPoly(tractGeom[v])\n",
    "        plotPoly(countyGeom[mC])\n",
    "        DR = 0.2\n",
    "        plt.plot([cutPoint.x - DR * math.cos(sliceAngle), cutPoint.x + DR * math.cos(sliceAngle)],\n",
    "                 [cutPoint.y - DR * math.sin(sliceAngle), cutPoint.y + DR * math.sin(sliceAngle)] )  #plotting the original pizza cut\n",
    "        plt.show()\n",
    " \n",
    "    #print(\"our final halfPop3 vs aDP is\",halfPop3, targPop,'for angle (deg) =',r3(sliceAngle*180./math.pi))\n",
    "    if deebug:\n",
    "        plotPoly(tractCP[t].buffer(0.02))\n",
    "        for v in halfList3:\n",
    "            plotPoly(tractGeom[v])\n",
    "        plotPoly(countyGeom[mC])\n",
    "        DR = 0.2\n",
    "        plt.plot([cutPoint.x - DR * math.cos(sliceAngle), cutPoint.x + DR * math.cos(sliceAngle)],\n",
    "                [cutPoint.y - DR * math.sin(sliceAngle), cutPoint.y + DR * math.sin(sliceAngle)] )  #plotting the original pizza cut\n",
    "        plt.show()\n",
    "        pause = input(\"ready to continue?\")\n",
    "    halfListB, halfList3B, halfPopB, halfPop3B = list(), list(), 0., 0.\n",
    "    for v in mCTL:\n",
    "        if v not in halfList:\n",
    "            halfListB.append(v)\n",
    "            halfPopB += tractPop3[v]\n",
    "        if v not in halfList3:\n",
    "            halfList3B.append(v)\n",
    "            halfPop3B += tractPop3[v]\n",
    "    mC_ppC_nList, mC_ppC_nList3 = list(), list()  #ppC neighbors of mC tracts (for building Columbiana piece)        \n",
    "    needsPPC, needsPPC3 = False, False\n",
    "    if halfPopB < 0.95 * aDP :  #need to add a little Columbiana\n",
    "        needsPPC = True\n",
    "        nDist = list()\n",
    "        popGap = aDP - halfPopB\n",
    "        mC_ppC_nList = list()\n",
    "        for v in halfListB:\n",
    "            for n in neighborList[v]:\n",
    "                if n in countyTractList[ppC] and n not in mC_ppC_nList:\n",
    "                    mC_ppC_nList.append(n)\n",
    "                    nDist.append(tractCP[n].distance(tractCP[t]) )\n",
    "        if mC_ppC_nList == list():\n",
    "            print(\"no\",ppC,\"county neighbors to halfListB for tract\",t,\"PUNT! for this tract\")\n",
    "    if mC_ppC_nList == list() and needsPPC:\n",
    "        needsPPC = False  #don't bother trying below (tho \"continue\" should prevent this)\n",
    "        continue  \n",
    "    if halfPop3B < 0.95 * aDP :  #need to add a little Columbiana\n",
    "        needsPPC3 = True\n",
    "        nDist3 = list()\n",
    "        popGap3 = aDP - halfPop3B\n",
    "        mC_ppC_nList3 = list()\n",
    "        for v in halfList3B:\n",
    "            for n in neighborList[v]:\n",
    "                if n in countyTractList[ppC] and n not in mC_ppC_nList3:\n",
    "                    mC_ppC_nList3.append(n)\n",
    "                    nDist3.append(tractCP[n].distance(tractCP[t]) )\n",
    "        if mC_ppC_nList3 == list():\n",
    "            print(\"no\",ppC,\"county neighbors to halfList3B for tract\",t,\"PUNT! for this tract\")\n",
    "    if mC_ppC_nList3 == list() and needsPPC3:\n",
    "        needsPPC3 = False #don't bother trying below (tho \"continue\" should prevent this)\n",
    "        continue\n",
    "    # 3/15/23 idea to replace below - add from nList until exhausted, then nlist of nlist, layer by layer\n",
    "    if needsPPC:\n",
    "        farthestT = mC_ppC_nList[nDist.index(np.max(nDist)) ]\n",
    "        #print(\"halfPopB, farthestT, mC_ppC_nList\",halfPopB, farthestT, mC_ppC_nList)\n",
    "        while halfPopB < 0.95*aDP:\n",
    "            halfListB, halfPopB = halfListB + [farthestT], halfPopB + tractPop[farthestT]\n",
    "            mC_ppC_nList.remove(farthestT)\n",
    "            nDist.remove(np.max(nDist))\n",
    "            if mC_ppC_nList == list() : #had only one toucher which we have taken.  rebuild list from here\n",
    "                for v in neighborList[farthestT]:\n",
    "                    if v in countyTractList[ppC] and v not in halfListB:\n",
    "                        mC_ppC_nList.append(v)\n",
    "                        nDist.append(tractCP[v].distance(tractCP[t]) )\n",
    "            farthestT = mC_ppC_nList[nDist.index(np.max(nDist)) ]\n",
    "        #ppCTL, FcutPoint = countyTractList[ppC], get_PCP(halfListB,tractCP, tractPop3)\n",
    "        #ppClist, ppCpop = cutVtdSnap(popGap, 0.05*aDP, tractCP, ppCTL, neighborList, tractPop, sliceAngle, FcutPoint)\n",
    "        #halfListB, halfPopB = halfListB + ppClist, halfPopB + ppCpop\n",
    "    if needsPPC3:\n",
    "        farthestT3 = mC_ppC_nList3[nDist3.index(np.max(nDist3)) ]\n",
    "        ppCTL, FcutPoint3 = countyTractList[ppC], get_PCP(halfList3B,tractCP, tractPop3)       \n",
    "        list3B, pop3B, nSplits = tryNoSplit(farthestT3, popGap3, 0.05*aDP, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,[],\n",
    "                                          countyMuniList[ppC],tractCP, neighborList,tractPop3,0)\n",
    "        halfList3B, halfPop3B = halfList3B+list3B, halfPop3B+pop3B\n",
    "        \n",
    "    if np.min([halfPop, halfPopB, halfPop3, halfPop3B]) > 0.95*aDP and np.max([halfPop, halfPopB, halfPop3, halfPop3B]) < 1.05*aDP:\n",
    "        if isContiguous(halfList,neighborList) and isContiguous(halfListB,neighborList):\n",
    "            if isContiguous(halfList3,neighborList) and isContiguous(halfList3B,neighborList):\n",
    "                print(\"      t\",t,r3(180.*sliceAngle/math.pi),\"is a good deg Angle for creating 2 districts in county\",mC)\n",
    "                LISTS.append(  [ halfList, halfListB ] )\n",
    "                LISTS3.append( [ halfList3,halfList3B] )\n",
    "                goodMahoningAngles.append(sliceAngle)\n",
    "                goodMahoningStarterTracts.append(t)\n",
    "                \n",
    "            else:\n",
    "                print(\"failed contiguity for muniSnap for starter tract, angle\",t,r3(180.*sliceAngle/math.pi))\n",
    "        else:\n",
    "            print(\"failed contiguity for vtdSnap for starter tract, angle\",t,r3(180.*sliceAngle/math.pi))\n",
    "    else:\n",
    "        print(t,r3(180.*sliceAngle/math.pi),\"t, deg, FAILED TO meet district pops in county\",mC)\n",
    "print(\"we created\",len(LISTS),\"good 2-district plans out of\",nCuts,\"tries in county\",mC)    \n",
    "MahoningLISTS, MahoningLISTS3 = LISTS.copy(), LISTS3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f8fe4efd-988d-43e5-92ce-54074f111348",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time for Mahoning tracts to vote.  Columb straddlers don't influence, so don't vote\n",
      "Voting done.  The most popular plan was 52 with 0.131 of the vote\n",
      "Voting done.  The most popular plan was 73 with 0.173 of the vote\n",
      "let us write the HD weights to a file for sub-region Mahoning Mahoning\n"
     ]
    }
   ],
   "source": [
    "print(\"time for Mahoning tracts to vote.  Columb straddlers don't influence, so don't vote\" )  \n",
    "currentRegionNo, fName = 8, \"Mahoning\"  #copy this code block from Lake\n",
    "areaVoters = countyTractList[49] \n",
    "rLISTS = [MahoningLISTS.copy(), MahoningLISTS3.copy() ]\n",
    "rWeights =  [setPlanWeights(areaVoters,  rLISTS[0], tractCP, tractPop3), setPlanWeights(areaVoters, rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"let us write the HD weights to a file for sub-region\",fName,regionName[currentRegionNo])\n",
    "date = \"04May\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8ad32004-f39a-470e-9bd3-2df945158910",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "circles denote tracts that drove good 2-district maps, x's failed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30 regional maps included some Columbiana out of 99\n"
     ]
    }
   ],
   "source": [
    "print(\"circles denote tracts that drove good 2-district maps, x's failed\") #OK that half failed\n",
    "for t in countyTractList[mC]:\n",
    "    if t in goodMahoningStarterTracts:\n",
    "        plt.scatter(tractCP[t].x, tractCP[t].y, marker=\"o\")\n",
    "    else:\n",
    "        plt.scatter(tractCP[t].x, tractCP[t].y, marker=\"x\")\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyPCP[mC].buffer(0.02))\n",
    "plt.show()\n",
    "nColumbiana = 0\n",
    "for L3 in MahoningLISTS3:\n",
    "    hasColumbiana = False\n",
    "    for L in L3:\n",
    "        for v in countyTractList[ppC]:\n",
    "            if v in L:\n",
    "                hasColumbiana = True\n",
    "                break\n",
    "    if hasColumbiana:\n",
    "        nColumbiana +=1\n",
    "print(nColumbiana,\"regional maps included some Columbiana out of\",len(LISTS3) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "2a36139f-d0c8-41eb-934a-a13e20782799",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IDing straddlers in Dayton area / Montgomery.  7 districts (4 in Montgomery, 1 each Greene, Clark, 1 Mtgmry straddler\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"IDing straddlers in Dayton area / Montgomery.  7 districts (4 in Montgomery, 1 each Greene, Clark, 1 Mtgmry straddler\")\n",
    "mC = 56 #main county = Montgomery\n",
    "aDP = avgDistrictPop\n",
    "toler = 0.05 * aDP\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "#currentRegionNo = 6   #\"Dayton\"\n",
    "pairedCountyList = [28, 11, 54, 67, 8]  #Greene, Clark, Miami, Preble, Butler (Darke too far)\n",
    "spcGroups = [ [11 ], [28, 54], [], [8,18],[67] ]  #these are possible third counties in a Mtgmry-PPC-3rd straddler \n",
    "ppC = -999  #a default flag to not draw a whole district in any neighboring counties\n",
    "ppC = pairedCountyList[0]  #primary paired county (e.g. Greene - 28)\n",
    "luringCountyList = [28,11,54,67,8, 28,11,54,67,8, 11,54,67,8]   #Warren is 2-whole, Darke too far away.\n",
    "#                                       De-emphasize Greene slightly due to its constraints\n",
    "forcedTractList = []  #for Montgomery, straddlers can be anywhere.  [For Lucas, we constrain this]\n",
    "ppCmaxPop = countyPop[ppC] % avgDistrictPop  #the max population from the primary pairing county that can be used in a straddling district\n",
    "targetRemnantPop = avgDistrictPop * (countyDistricts[mC]%1.)  #the natural pop left after filling whole districts in main county\n",
    "luringList = []\n",
    "for c in luringCountyList:\n",
    "    for tt in countyTractList[c]:\n",
    "        luringList.append(tt)   #these tracts in nearby counties \"lure\" HDs which contain them to center straddler HDs\n",
    "forcedGeom = countyGeom[mC]   #default; we do not force any tracts to be in the original HDPoly's of straddler districts\n",
    "if len(forcedTractList) > 0:\n",
    "    forcedGeom = tractCP[forcedTractList[0]]\n",
    "    for T in forcedTractList:\n",
    "        forcedGeom = forcedGeom.union(tractCP[T])\n",
    "countyStraddlerList[mC] = get_straddlers(targetRemnantPop, countyPop[mC], countyTractList[mC], luringList, forcedGeom, HDPoly, tractCP, tractPop3)\n",
    "mC_EnsnaredPop = 0.\n",
    "for t in countyTractList[mC]:\n",
    "    if t in countyStraddlerList[mC]:\n",
    "        HDtype[t] = \"straddlerMaker\"        \n",
    "        plt.scatter(HDCPx[t],HDCPy[t],marker=\"x\", label=\"generated a straddler HD\")\n",
    "        mC_EnsnaredPop += tractPop3[t]\n",
    "    else:\n",
    "        HDtype[t] = \"notAstraddlerMaker\"\n",
    "        plt.scatter(HDCPx[t],HDCPy[t],marker=\".\", label=\"not a straddler generator\")\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "id": "dc77667f-fcd7-4989-819e-f3e99bc6b93a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lets visualize muniPops near Montgomery\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uh oh, this is really blocky, many cities > 0.2 aDP\n"
     ]
    }
   ],
   "source": [
    "print(\"lets visualize muniPops near Montgomery\")   #USUALLY SKIP THIS\n",
    "cList = neighborCountyList[56].copy()\n",
    "cList.append(56)\n",
    "cList.remove(82)\n",
    "plotPoly(countyGeom[56])\n",
    "#cList = [56]\n",
    "for c in cList:\n",
    "    for m in countyMuniList[c]:\n",
    "        if muniPop[m] > 20000:\n",
    "            plt.text(muniPCP[m].x-0.03,muniPCP[m].y,int(muniPop[m]),fontsize=6)\n",
    "            plotPoly(muniGeom[m])\n",
    "plt.show()\n",
    "print(\"Uh oh, this is really blocky, many cities > 0.2 aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "38d3733f-b38f-473e-adc6-8d9c7a9c0665",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3515 118764.0 118432.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3515 1 t,try no. 2-district pops were 239599.0 237397.0 vs tgts 239246 239237\n",
      "3515 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3566 120895.0 119956.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3566 1 t,try no. 2-district pops were 240283.0 235323.0 vs tgts 238944 238722\n",
      "3566 2 t,try no. 2-district pops were 238794.0 237222.0 vs tgts 238944 238722\n",
      "3566 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 2\n",
      "3239 119587.0 118060.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3239 1 t,try no. 2-district pops were 239012.0 235015.0 vs tgts 237854 238661\n",
      "3239 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3338 120988.0 120057.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3338 1 t,try no. 2-district pops were 238032.0 236742.0 vs tgts 238852 239157\n",
      "3338 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3561 118900.0 118147.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3561 1 t,try no. 2-district pops were 238374.0 241884.0 vs tgts 239604 241330\n",
      "3561 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3494 117831.0 115815.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3494 1 t,try no. 2-district pops were 236763.0 240576.0 vs tgts 238376 238631\n",
      "3494 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3358 119395.0 114241.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3358 1 t,try no. 2-district pops were 237559.0 233881.0 vs tgts 238262 238394\n",
      "3358 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "adding full county 67 40999.0 and we still need 18649\n",
      "3363 121425.0 119073.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3363 1 t,try no. 2-district pops were 239269.0 239976.0 vs tgts 238885 239418\n",
      "3363 2 t,try no. 2-district pops were 239791.0 236466.0 vs tgts 238885 239418\n",
      "3363 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 2\n",
      "adding full county 67 40999.0 and we still need 17622\n",
      "3565 120472.0 122743.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3565 1 t,try no. 2-district pops were 238854.0 237344.0 vs tgts 238372 238571\n",
      "3565 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3341 118966.0 121093.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3341 1 t,try no. 2-district pops were 238878.0 238024.0 vs tgts 238345 240068\n",
      "3341 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3257 120212.0 116570.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3257 1 t,try no. 2-district pops were 236683.0 239476.0 vs tgts 237708 239587\n",
      "3257 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3355 118084.0 121137.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3355 1 t,try no. 2-district pops were 238919.0 241048.0 vs tgts 238926 238117\n",
      "3355 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3499 119013.0 114241.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3499 1 t,try no. 2-district pops were 237215.0 238516.0 vs tgts 238869 238394\n",
      "3499 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3262 120221.0 119850.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3262 1 t,try no. 2-district pops were 237163.0 239976.0 vs tgts 238326 240272\n",
      "3262 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3243 119586.0 120456.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3243 1 t,try no. 2-district pops were 236871.0 237414.0 vs tgts 237918 239581\n",
      "3243 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "loop,Rratio,SNAPPOP,nMuni 22 1.0896378288269042 16454.0 3\n",
      "loop,Rratio,SNAPPOP,nMuni 23 1.0896380672454833 16454.0 3\n",
      "3342 118201.0 117829.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3342 1 t,try no. 2-district pops were 11236.0 243002.0 vs tgts 238998 238613\n",
      "3342 2 t,try no. 2-district pops were 238648.0 234158.0 vs tgts 238998 238613\n",
      "3342 3 t,try no. 2-district pops were 239882.0 239976.0 vs tgts 238998 238613\n",
      "3342 4 t,try no. 2-district pops were 238468.0 239976.0 vs tgts 238998 238613\n",
      "3342 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 4\n",
      "working on tract 3560\n",
      "3560 120599.0 119830.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3560 1 t,try no. 2-district pops were 239703.0 242102.0 vs tgts 238204 239573\n",
      "3560 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3252 119797.0 116244.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3252 1 t,try no. 2-district pops were 239269.0 235358.0 vs tgts 238760 238877\n",
      "3252 2 t,try no. 2-district pops were 239353.0 241395.0 vs tgts 238760 238877\n",
      "3252 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 2\n",
      "3563 121465.0 116129.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3563 1 t,try no. 2-district pops were 240785.0 240682.0 vs tgts 239367 240249\n",
      "3563 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3564 120004.0 118614.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3564 1 t,try no. 2-district pops were 237736.0 237605.0 vs tgts 238741 240249\n",
      "3564 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "adding full county 67 40999.0 and we still need 16667\n",
      "3261 118702.0 117069.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3261 1 t,try no. 2-district pops were 238427.0 234248.0 vs tgts 237894 237085\n",
      "3261 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3272 119532.0 115629.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3272 1 t,try no. 2-district pops were 240874.0 238373.0 vs tgts 238811 240326\n",
      "3272 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3493 118203.0 120850.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3493 1 t,try no. 2-district pops were 239706.0 241577.0 vs tgts 238513 239775\n",
      "3493 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3327 117272.0 116592.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3327 1 t,try no. 2-district pops were 241489.0 238018.0 vs tgts 240866 240157\n",
      "3327 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "adding full county 67 40999.0 and we still need 18459\n",
      "3263 121452.0 117118.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3263 1 t,try no. 2-district pops were 237337.0 234686.0 vs tgts 238790 237060\n",
      "3263 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "working on tract 3500\n",
      "adding full county 67 40999.0 and we still need 18174\n",
      "3500 119305.0 117555.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3500 1 t,try no. 2-district pops were 236811.0 237021.0 vs tgts 238648 236842\n",
      "3500 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3359 118146.0 122150.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3359 1 t,try no. 2-district pops were 241486.0 242893.0 vs tgts 240884 239329\n",
      "3359 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3324 119544.0 118127.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3324 1 t,try no. 2-district pops were 240856.0 237881.0 vs tgts 239542 240249\n",
      "3324 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "adding full county 67 40999.0 and we still need 16643\n",
      "T, loop,Rratio,SNAPPOP,nTracts included 3266 19 2.0009961853027343 1807.0 1\n",
      "T, loop,Rratio,SNAPPOP,nTracts included 3266 20 2.000998092651367 1807.0 1\n",
      "T, loop,Rratio,SNAPPOP,nTracts included 3266 21 2.0009990463256835 1807.0 1\n",
      "T, loop,Rratio,SNAPPOP,nTracts included 3266 22 2.0009995231628417 1807.0 1\n",
      "T, loop,Rratio,SNAPPOP,nTracts included 3266 23 2.000999761581421 1807.0 1\n",
      "loop,Rratio,SNAPPOP,nMuni 22 2.0009995231628417 8556.0 1\n",
      "loop,Rratio,SNAPPOP,nMuni 23 2.000999761581421 8556.0 1\n",
      "3266 104350.0 115352.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3266 1 t,try no. 2-district pops were 236288.0 236109.0 vs tgts 237882 239123\n",
      "3266 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "adding full county 67 40999.0 and we still need 17523\n",
      "3265 120250.0 114592.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3265 1 t,try no. 2-district pops were 237871.0 243059.0 vs tgts 238322 239503\n",
      "3265 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "working on tract 3360\n",
      "3360 118087.0 114780.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3360 1 t,try no. 2-district pops were 241306.0 239233.0 vs tgts 240300 241063\n",
      "3360 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "working on tract 3340\n",
      "3340 117012.0 121271.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3340 1 t,try no. 2-district pops were 237434.0 233792.0 vs tgts 238709 237827\n",
      "3340 2 t,try no. 2-district pops were 240998.0 238375.0 vs tgts 238709 237827\n",
      "3340 3 t,try no. 2-district pops were 238539.0 234623.0 vs tgts 238709 237827\n",
      "3340 4 t,try no. 2-district pops were 237480.0 239658.0 vs tgts 238709 237827\n",
      "3340 5 t,try no. 2-district pops were 238619.0 239980.0 vs tgts 238709 237827\n",
      "3340 6 t,try no. 2-district pops were 240467.0 235948.0 vs tgts 238709 237827\n",
      "3340 7 t,try no. 2-district pops were 241899.0 233895.0 vs tgts 238709 237827\n",
      "3340 8 t,try no. 2-district pops were 238581.0 234629.0 vs tgts 238709 237827\n",
      "3340 9 t,try no. 2-district pops were 241936.0 10127.0 vs tgts 238709 237827\n",
      "3340 10 t,try no. 2-district pops were 238687.0 10127.0 vs tgts 238709 237827\n",
      "adding full county 67 40999.0 and we still need 17348\n",
      "3593 116685.0 114762.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3593 1 t,try no. 2-district pops were 240307.0 242889.0 vs tgts 238235 239418\n",
      "3593 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3238 119109.0 120850.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3238 1 t,try no. 2-district pops were 237034.0 236698.0 vs tgts 238513 239775\n",
      "3238 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3264 120439.0 117382.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3264 1 t,try no. 2-district pops were 237402.0 238144.0 vs tgts 237632 240326\n",
      "3264 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "working on tract 3260\n",
      "adding full county 67 40999.0 and we still need 16849\n",
      "3260 121418.0 120848.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3260 1 t,try no. 2-district pops were 237343.0 238839.0 vs tgts 237985 238531\n",
      "3260 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3557 118343.0 121137.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3557 1 t,try no. 2-district pops were 238462.0 242269.0 vs tgts 238110 238117\n",
      "3557 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3492 118654.0 120850.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3492 1 t,try no. 2-district pops were 238095.0 237583.0 vs tgts 238189 239775\n",
      "3492 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "adding full county 67 40999.0 and we still need 16771\n",
      "3451 118001.0 115317.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3451 1 t,try no. 2-district pops were 233994.0 237348.0 vs tgts 237946 237961\n",
      "3451 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 1\n",
      "3301 118305.0 118790.0 ( 119186.343 ) t,straddler snap and snap3, (target)\n",
      "3301 1 t,try no. 2-district pops were 236740.0 238189.0 vs tgts 238321 238877\n",
      "3301 2 t,try no. 2-district pops were 237309.0 236174.0 vs tgts 238321 238877\n",
      "3301 has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try 2\n"
     ]
    }
   ],
   "source": [
    "# functional as of 3/11/23\n",
    "mC = 56 #main county = Montgomery\n",
    "aDP = avgDistrictPop\n",
    "toler = 0.05 * aDP\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "#currentRegionNo = 6   #\"Dayton\"\n",
    "pairedCountyList = [28, 11, 54, 67, 8]  #Greene, Clark, Miami, Preble, Butler (Darke too far)\n",
    "spcGroups = [ [11 ], [28, 54], [], [8,18],[67] ]  #these are possible third counties in a Mtgmry-PPC-3rd straddler \n",
    "\n",
    "Dayton7LISTS = list()  #a list of 7 lists of vtds in a 7-district Dayton-area vtd-snapped regional plan\n",
    "Dayton7LISTS3 = list()  #same, for muni-snapped\n",
    "Dayton7Starters = list() #the starter vtd No for the two above lists\n",
    "\n",
    "for t in countyStraddlerList[mC]:  #obtained in above block\n",
    "    homeM = tractMuniNo[t]\n",
    "    nSplits = 0  #tracking total splits in all counties composing this straddler district.  Force to 0-1\n",
    "    HDangle = [HDangle0[t],  HDangle1[t], HDangle2[t], HDangle3[t] ]\n",
    "    HDradius = [HDradius0[t], HDradius1[t], HDradius2[t], HDradius3[t] ]\n",
    "    targetICP = avgDistrictPop * (countyDistricts[mC]%1.)  #target in-county Pop.  reset in case we adjusted for previous tract\n",
    "    if t%20 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    adjCountyHDPolyPop = [0.] * nCounties  #for Montgomery, we need special rules based on which adjacent counties we spill into\n",
    "    for cc in pairedCountyList:\n",
    "        for tt in countyTractList[cc]:\n",
    "            overlap = HDPoly[t].intersection(tractGeom[tt]).area\n",
    "            if overlap > 0:\n",
    "                adjCountyHDPolyPop[cc] += tractPop[tt] * overlap / tractGeom[tt].area\n",
    "    ppC = adjCountyHDPolyPop.index(np.max(adjCountyHDPolyPop) ) #the primary pairing county had the most pop in this t's orig HD\n",
    "    spClist = spcGroups[pairedCountyList.index(ppC)]  #list of possible spc's, given the selected primary pairing county\n",
    "    spC = -999  #secondary pairing county\n",
    "    if len(spClist) > 0:\n",
    "        spC = spClist[0]  #default - secondary pairing county is the first in the ppC's spcList\n",
    "        for cc in spClist:  #nothing happens here unless >1 possible spc's listed\n",
    "            if adjCountyHDPolyPop[cc] > adjCountyHDPolyPop[spC] :  #found a stronger spc\n",
    "                spC = cc\n",
    "    bigClist = [pairedCountyList[0], pairedCountyList[1] ]  #list of neighbor counties that exceed a whole district\n",
    "    isWCwhole = [\"yes\"]*len(bigClist)  #used later in creating a full Greene district\n",
    "    for wC in bigClist:  \n",
    "        if ppC == wC or spC == wC:  #flag that we already took a bite out of this county, so its whole-county district = remainder\n",
    "            isWCwhole[bigClist.index(wC)] =  \"no\" \n",
    "    isPPCconstrained = False\n",
    "    if ppC == bigClist[0] or spC == bigClist[0]:  #shade down strad pop when Mtmry paired w/ GREENE & Clark (4.51 + 1.14 + 1.41) %1\n",
    "        targetICP = avgDistrictPop * (1.03 - (countyDistricts[ppC]+countyDistricts[spC])%1 )\n",
    "        isPPCconstrained = True\n",
    "                \n",
    "    HDtractList[t] = []\n",
    "    toler2 = toler*0.7  #reduce each sub-district's slop to reduce error propagation\n",
    "    mClist, mCsnapPop = HDvtdSnap(t, targetICP, toler2, tractCP, countyTractList[mC],neighborList,tractPop3,\n",
    "                                    HDangle, HDradius) #inCounty vtd's for the straddler district\n",
    "    mClist3, mcML3, mCsnapPop3, splitMuni = HDmuniSnap(t,targetICP, toler2, muniTractList, tractCP,countyMuniList[mC],\n",
    "                                                         muniNeighborList,tractPop3, muniPop, muniPCP, HDangle, HDradius,homeM)\n",
    "    popGap = targetICP - mCsnapPop3\n",
    "    mClist3B, mCsnapPop3B = list(),0.\n",
    "    if (abs(popGap) > toler2) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "        if popGap < 0  :\n",
    "            raise Exception(\"error! mCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "        mClist3B, mCsnapPop3B = getFillList3noSplit(t, popGap, toler2, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                    mcML3, countyMuniList[mC],tractCP, neighborList,tractPop3)\n",
    "        if mCsnapPop3B == 0. :\n",
    "            print(\"uh oh, failed to create whole-muni in inCounty part of straddler district for tract\",t,\"we are short by\",popGap)\n",
    "        \n",
    "        mClist3, mCsnapPop3 = mClist3 + mClist3B, mCsnapPop3 + mCsnapPop3B  #completing in the inCounty partial despite splitM flag\n",
    "    \n",
    "    snappedHDpop[t] = mCsnapPop\n",
    "    snappedHD3pop[t] = mCsnapPop3\n",
    "    HDtractList[t]  = mClist.copy()\n",
    "    HDtractList3[t] = mClist3.copy()\n",
    "    #print(\"t,mCsnapPop, mCsnapPop3 are\",t,mCsnapPop, mCsnapPop3,\"vs tgt\",targetICP)\n",
    "    isFullppC, isFullppC3 = False, False\n",
    "    if countyPop[ppC] > avgDistrictPop: #pairing county constrained, nontaken must form whole district(s)\n",
    "        ppCtgtPop, ppCtgtPop3 = countyPop[ppC]%avgDistrictPop, countyPop[ppC]%avgDistrictPop\n",
    "    else:\n",
    "        ppCtgtPop, ppCtgtPop3 = avgDistrictPop - mCsnapPop, avgDistrictPop - mCsnapPop3\n",
    "        if countyPop[ppC] < ppCtgtPop + toler:\n",
    "            ppClist, ppCsnapPop, isFullppC = countyTractList[ppC], countyPop[ppC], True\n",
    "            print(\"adding full county\",ppC,countyPop[ppC],\"and we still need\",int(avgDistrictPop-snappedHDpop[t]-countyPop[ppC]) )\n",
    "        if countyPop[ppC] < ppCtgtPop3 + toler:\n",
    "            ppClist3, ppCsnapPop3, isFullppC3 = countyTractList[ppC], countyPop[ppC], True       \n",
    "        \n",
    "    #Note: in previous version, we forced Greene and Clarke partials to be contiguous.  Here, allow linking thru Mtgmry\n",
    "    if not isFullppC:\n",
    "        ppClist, ppCsnapPop = HDvtdSnap(t, ppCtgtPop, toler2, tractCP, countyTractList[ppC],neighborList,tractPop3,\n",
    "                                        HDangle, HDradius) \n",
    "    if not isFullppC3:\n",
    "        ppClist3, ppcML3, ppCsnapPop3, splitMuni = HDmuniSnap(t,ppCtgtPop3, toler2, muniTractList, tractCP, countyMuniList[ppC],\n",
    "                                                             muniNeighborList,tractPop3, muniPop, muniPCP, HDangle, HDradius)  \n",
    "        popGap = ppCtgtPop3 - ppCsnapPop3\n",
    "        #print(t,int(ppCtgtPop3), ppCsnapPop3, \"t,tgt ppC3, actual\",ppcML3,\"=ppcML3\")\n",
    "        if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "            if popGap < 0  :\n",
    "                print(\"munisnap pop of\",mCsnapPop3,\"in\",mC,\"and\",ppCsnapPop3,\"in cty\",ppC,\"vs tgt\",ppCtgtPop3)\n",
    "                raise Exception(\"error! ppCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            # 3/4/23 NOTE:  WE COULD TRY A WORKAROUND IF ppcML3 is blank.  Requires us to ID all ppC muni's contiguous to mC HDlist\n",
    "            ppClist3B, ppCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler2, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,ppcML3,\n",
    "                                                        countyMuniList[ppC],tractCP, neighborList,tractPop3,nSplits)        \n",
    "            ppClist3, ppCsnapPop3 = ppClist3 + ppClist3B, ppCsnapPop3 + ppCsnapPop3B\n",
    "        #print(t,ppCsnapPop, ppCsnapPop3,\"ppC pops vs tgts\",int(ppCtgtPop), int(ppCtgtPop3))\n",
    "    \n",
    "    HDtractList[t]  += ppClist\n",
    "    HDtractList3[t] += ppClist3\n",
    "    snappedHDpop[t]  += ppCsnapPop\n",
    "    snappedHD3pop[t] += ppCsnapPop3 \n",
    "    #print(\"after adding ppC, t, HDpop and pop3 are\",t, snappedHDpop[t], snappedHD3pop[t])\n",
    "    \n",
    "    spClist, spClist3, spCsnapPop, spCsnapPop3 = [],[],0,0\n",
    "    mopupTgtPop = avgDistrictPop - snappedHDpop[t]  \n",
    "    if mopupTgtPop > 0.05 * avgDistrictPop: #district not yet complete; add the mopup\n",
    "        mopupCounty = spC\n",
    "        mopupPool = countyTractList[mopupCounty].copy()\n",
    "        closePt = get_PCP(HDtractList[t], tractCP, tractPop3)  #NEWWWWWWWWWWWWWWWWW\n",
    "        spCcT = getClosestTract(closePt, mopupPool, tractCP)  #mopupCounty closestTract to the straddler-initiating tract #tractCP[t]\n",
    "        spClist, spCsnapPop = HDvtdSnap(t, mopupTgtPop, toler, tractCP, mopupPool,neighborList,tractPop3,\n",
    "                                        HDangle, HDradius,spCcT)\n",
    "    mopupTgtPop3 = avgDistrictPop - snappedHD3pop[t]\n",
    "    if mopupTgtPop3 > 0.05 * avgDistrictPop and nSplits <=1 : #if we already needed two splits, don't bother\n",
    "        mopupCounty = spC\n",
    "        mopupPool = countyTractList[mopupCounty].copy()\n",
    "        #spCcT = getClosestTract(tractCP[t], mopupPool, tractCP)  #mopupCounty closestTract to the straddler-initiating tract\n",
    "        spClist3, spcML3, spCsnapPop3, splitMuni = HDmuniSnap(t, mopupTgtPop3, toler, muniTractList, tractCP, countyMuniList[spC],\n",
    "                                                               muniNeighborList,tractPop3,muniPop, muniPCP,HDangle, HDradius)\n",
    "        popGap = mopupTgtPop3 - spCsnapPop3 \n",
    "        if abs(popGap) > toler:\n",
    "            if popGap < 0  :\n",
    "                raise Exception(\"error! spCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "                \n",
    "            # 3/4/23 NOTE:  WE COULD TRY A WORKAROUND IF spcML3 is blank.  Requires us to ID all spC muni's contiguous to growing HDlist\n",
    "            spClist3B, spCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,spcML3,\n",
    "                                                          countyMuniList[spC],tractCP, neighborList,tractPop3,nSplits)\n",
    "            spClist3, spCsnapPop3 = spClist3 + spClist3B, spCsnapPop3 + spCsnapPop3B\n",
    "            \n",
    "    HDtractList[t]  += spClist\n",
    "    HDtractList3[t] += spClist3\n",
    "    snappedHDpop[t]  += spCsnapPop\n",
    "    snappedHD3pop[t] += spCsnapPop3 \n",
    "    print(t,snappedHDpop[t], snappedHD3pop[t],\"(\",r3(avgDistrictPop) ,\") t,straddler snap and snap3, (target)\")\n",
    "    stradPoly,stradPoly3 = tractCP[t], tractCP[t]  #for debugging only\n",
    "    for v in HDtractList[t]:                       #debug\n",
    "        stradPoly = stradPoly.union(tractGeom[v]) #debug\n",
    "    for v in HDtractList3[t]:                          #debug\n",
    "        stradPoly3 = stradPoly3.union(tractGeom[v])  #debug\n",
    "    \n",
    "    LISTS = [HDtractList[t]]  #this was the first of seven districts to be drawn for this mC straddler-tract trigger\n",
    "    LISTS3 = [HDtractList3[t]]\n",
    "    #now draw whole Greene.  If the Mtgmry straddler did not include Greene, constrained to start from west\n",
    "    # then,  draw a whole Clark district.  If the Mtgmry straddler did not include Clark, unconstrained random pie cut\n",
    "    targetPop = avgDistrictPop\n",
    "    tolerPop = 0.03 * targetPop   \n",
    "    for i,wC in enumerate(bigClist):\n",
    "        wClist, wClist3 = list(), list()\n",
    "        if isWCwhole[i] == \"no\":  #magic partial already drawn into straddler.  Remainder of this county forms whole district\n",
    "            for tt in countyTractList[wC]:\n",
    "                if tt not in HDtractList[t]:\n",
    "                    wClist.append(tt)\n",
    "                if tt not in HDtractList3[t]:\n",
    "                    wClist3.append(tt)\n",
    "        else:  #grow a whole district via pizza cut.  ID the \"starter\" tract closest to the HDCP to orient the cut (new 3/23)\n",
    "            wCpool = countyTractList[wC]           \n",
    "            cutPoint = countyPCP[wC]\n",
    "            wCcT = getClosestTract(tractCP[t], wCpool, tractCP)  #wholeCounty tract closest to the HD-centering tract\n",
    "            sliceAngle = getSliceAngle(tractCP[t], cutPoint, tractCP[wCcT])\n",
    "            wClist, wCpop = cutVtdSnap(aDP, toler, tractCP, wCpool, neighborList, tractPop3, sliceAngle, cutPoint,wCcT)\n",
    "            #pause = input(\"stop here\")\n",
    "            wClist3, wCML3, wCsnapPop3, splitMuni = cutMuniSnap(aDP, toler, muniTractList,tractCP, wCpool, muniNeighborList,\n",
    "                                                               tractPop3, muniPop, muniPCP, sliceAngle, cutPoint)\n",
    "            popGap = aDP - wCsnapPop3\n",
    "            if popGap > toler:  #needed to split a muni, but didn't\n",
    "                wClist3B, wCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,wCML3,\n",
    "                                                          countyMuniList[wC],tractCP, neighborList,tractPop3,nSplits)        \n",
    "                wClist3, wCsnapPop3 = wClist3 + wClist3B, wCsnapPop3 + wCsnapPop3B\n",
    "        LISTS.append(wClist)\n",
    "        LISTS3.append(wClist3)\n",
    "        \n",
    "    # now perform 1 + 2 bisections to form four whole Mtgmry districts from vtd's not drawn into the straddler HD\n",
    "    mCpool, mCpool3, muni2dPool3 = list(), list(), list()\n",
    "    mCpoolPop, mCpoolPop3 = 0., 0.\n",
    "    for v in countyTractList[mC]:\n",
    "        if v not in HDtractList[t]:  #i.e. not in this proposed straddler district\n",
    "            mCpool.append(v)\n",
    "            mCpoolPop += tractPop3[v]\n",
    "        if v not in HDtractList3[t]:\n",
    "            mCpool3.append(v)\n",
    "            mCpoolPop3 += tractPop3[v]\n",
    "            if tractMuniNo[v] not in muni2dPool3:\n",
    "                muni2dPool3.append(tractMuniNo[v])  #the muni's not in the straddler HD3 - used later in tryNoSplit 2district\n",
    "                \n",
    "    tgt2Pop =  2. * (countyPop[mC] - mCsnapPop )  / int(countyDistricts[mC])  #\"2.\" for 1st of two orthog pizzacuts\n",
    "    tgt2Pop3 = 2. * (countyPop[mC] - mCsnapPop3 ) / int(countyDistricts[mC])\n",
    "    maxPop, minPop = 2.04*aDP, 1.96*aDP #narrowing these a bit to increase success of later splitting 2 --> 4\n",
    "    tol2 = r3(max(0.02*aDP,min(maxPop - tgt2Pop , tgt2Pop - minPop)) ) #for simplicity, narrow the range so we won't fail on either side ...\n",
    "    tol3 = r3(max(0.02*aDP, min(maxPop - tgt2Pop3, tgt2Pop3- minPop)) ) # ... but force a positive small tolerance\n",
    "    #print(\"for vtdNo\",t,\"targetPops,(tol) for 2-districts w vtdSnap, muniSnap are\",int(tgt2Pop),\"(\",tol2, int(tgt2Pop3),\"(\",tol3,\")\" )\n",
    "    #print(\" ... based on countyPop, snappedHDpop, HD3pop of\", countyPop[mC], mCsnapPop, mCsnapPop3)\n",
    "\n",
    "    #print(\"total 4-district pops for vtd- and muni-snapped should be\",mCpoolPop, mCpoolPop3)\n",
    "    mCsnapPopCheck, mCsnapPop3Check = countyPop[mC], countyPop[mC]  #debug\n",
    "    for v in mCpool:  #debug\n",
    "        mCsnapPopCheck -= tractPop3[v]  #debug\n",
    "    for v in mCpool3:  #debug\n",
    "        mCsnapPop3Check -= tractPop3[v]  #debug\n",
    "    #print(\"checking mCsnap pop\",mCsnapPop, mCsnapPopCheck,\"and muni-based\",mCsnapPop3, mCsnapPop3Check)  #debug\n",
    "    cutPoint = get_PCP(mCpool, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts   \n",
    "    cutPoint3 = get_PCP(mCpool3, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts       \n",
    "    drawUnsuccessful = True\n",
    "    maxTries = 10 #10 #5 #15\n",
    "    nTries = 0\n",
    "    nInPlayTracts = len(mCpool)\n",
    "    sliceAngle = random.uniform(0., 2.*pi)\n",
    "    while drawUnsuccessful and nTries < maxTries:  #create the four districts triggered by a random initial cut angle ...\n",
    "        halfList, halfList3, halfPop, halfPop3 = [ [],[] ], [ [],[] ], [0.,0.], [0.,0.]\n",
    "        soFarSoGood = True\n",
    "        nTries +=1  #3/7/23 modification - do NOT specify a starter tract, let cutVTDsnap figure it out\n",
    "        needStarterTract = True\n",
    "        #while needStarterTract:\n",
    "        #    mCcT = mCpool[random.randint(nInPlayTracts)] #... based on a random vtd in these 4 districts for BOTH snapping technqs\n",
    "        #    if mCcT in mCpool3:\n",
    "        #        needStarterTract = False\n",
    "        sliceAngle += math.pi / maxTries  #we methodically try a series of possible cut angles.  (Some will lead to discontinuity)\n",
    "        sliceAngle3 = sliceAngle\n",
    "        # = getSliceAngle(tractCP[mCcT], cutPoint, tractCP[mCcT])  #we use same angle for vtd- & muni-snapped\n",
    "        halfList[0], halfPop[0] = cutVtdSnap(tgt2Pop, tol2, tractCP, mCpool, neighborList, tractPop3, sliceAngle, cutPoint)\n",
    "            \n",
    "        halfList3[0], hML, halfPop3[0], splitMuni = cutMuniSnap(tgt2Pop3, tol3, muniTractList,tractCP, mCpool3, muniNeighborList, \n",
    "                                                                tractPop3, muniPop, muniPCP, sliceAngle, cutPoint3)\n",
    "        popGap, fillPop = tgt2Pop3 - halfPop3[0], 0.\n",
    "        if abs(popGap) > tol3 :  #try alternate muni's, do not allow a split at this stage\n",
    "            #print(t,nTries,len(halfList3[0]),halfPop3[0], popGap, int(tol3),\"t,nTries, halfList3[0] len, pop & its popGap, tol3 prior to gFL3nS attempt\")\n",
    "            hT = getFarthestTract(cutPoint3, halfList3[0], tractCP)  #\"home\" tract to bias fill-in toward compactness\n",
    "            vlist, fillPop = getFillList3noSplit(hT, popGap, tol3,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,hML,\n",
    "                                             muni2dPool3, tractCP, neighborList, tractPop3)\n",
    "            halfList3[0] += vlist\n",
    "            halfPop3[0]  += fillPop\n",
    "            #print(\"gFL3nS added pop of\",fillPop,\"from\",len(vlist),\"vtds.  halfList3 len now\",len(halfList3[0]) )\n",
    "        \n",
    "        print(t,nTries,\"t,try no. 2-district pops were\",halfPop[0],halfPop3[0],\"vs tgts\",int(tgt2Pop), int(tgt2Pop3)) \n",
    "        if max(abs(halfPop[0]-tgt2Pop),abs(halfPop3[0]-tgt2Pop3) ) > 2.*0.05*aDP : #no hope of forming good districts\n",
    "            continue\n",
    "        for v in mCpool:\n",
    "            if v not in halfList[0]:\n",
    "                halfList[1].append(v)\n",
    "                halfPop[1]  += tractPop3[v]\n",
    "        muniPool3 = [list(), list()]  #the list of munis in each half of the 4-district set (we did not allow splitMuni in prev cut)\n",
    "        for v in mCpool3:\n",
    "            if v not in halfList3[0]:\n",
    "                halfList3[1].append(v)\n",
    "                halfPop3[1] += tractPop3[v]\n",
    "            elif tractMuniNo[v] not in muniPool3[0]:\n",
    "                muniPool3[0].append(tractMuniNo[v])\n",
    "        for m in countyMuniList[mC]:\n",
    "            if m not in muniPool3[0] and muniTractList[m][0] not in HDtractList3[t]:\n",
    "                muniPool3[1].append(m)\n",
    "        #print(\"and other-slice halfpops were\",halfPop[1], halfPop3[1])\n",
    "        #sIC = 0\n",
    "        #for v in countyTractList[mC]:\n",
    "        #    if v in HDtractList3[t]:\n",
    "        #        sIC +=1\n",
    "        #print(\"nVTDs in half-munisnap0, half-munisnap1, straddler-in-county, total county are\", len(halfList3[0]), len(halfList3[1]),sIC,len(countyTractList[mC]))\n",
    "        for v in countyTractList[mC]:\n",
    "            if v in halfList3[0] and v in halfList3[1]:\n",
    "                raise Exception(v,\"is in both halfLists for\",t,nTries)\n",
    "            if v in halfList3[1] and v in HDtractList3[t]:\n",
    "                raise Exception(v,\"is in 2nd HL3 and straddler for\",t,nTries)\n",
    "            if v in halfList3[0] and v in HDtractList3[t]:\n",
    "                raise Exception(v,\"is in 1st HL3 and straddler for\",t,nTries)\n",
    "        \n",
    "        if not ( isContiguous(halfList[1],neighborList) and isContiguous(halfList3[1],neighborList) ) :\n",
    "            soFarSoGood = False\n",
    "            continue  #try a new angle; early discontiguity\n",
    "        if min(abs(halfPop[0]-2*aDP),abs(halfPop[1]-2*aDP),abs(halfPop3[0]-2*aDP), abs(halfPop3[1]-2*aDP) ) > 0.08 * aDP:\n",
    "            soFarSoGood = False\n",
    "            continue #try a new angle; unlikely to hit pop range for qtr districts\n",
    "            \n",
    "        #OK, both 2-district sets are good.  Now try some angles for splitting each of these into \"qtr\" districts\n",
    "        qtrList, qtrList3, qtrPop, qtrPop3 = [list(),list(),list(),list()], [list(),list(),list(),list()], [0.]*4, [0.]*4\n",
    "        nGoodQtrCuts = 0\n",
    "        vtdAngle = [0.]*2\n",
    "        for i, HL in enumerate(halfList):  #first, attempt to generate four good qtr districts for vtd-snap\n",
    "            off2Pop = abs(0.5*halfPop[i] - aDP)\n",
    "            TOL = max( 0.02*aDP, toler - 0.5*off2Pop)  #tighten if 2-district off\n",
    "            qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            qtrCutNo = random.randint(maxQtrCutNo) #we'll make four 45deg-spaced cut tries, but randomize which to try first\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            while qLoopNo < maxQtrCutNo: #and haveCutGoodQtr; make all 4 possiblel cuts, keep trying for better\n",
    "                qAngle = sliceAngle + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo #0deg, 45deg, 90deg, 135deg vs orig cut\n",
    "                qElist, qtrPop[qE] = cutVtdSnap(0.5*halfPop[i], TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)  \n",
    "                qtrPop[qO] = halfPop[i] - qtrPop[qE]\n",
    "                qOlist = list()\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop[qE] - aDP) <= 0.05*aDP and abs(qtrPop[qO] - aDP) <= 0.05*aDP :  \n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList[qE], qtrList[qO] = qElist.copy(), qOlist.copy()\n",
    "                            vtdAngle[i] = qAngle #to preserve same angle for 1st try in later munisnap 4-district set\n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        if nGoodQtrCuts < 2:  #we failed to make four contiguous districts for vtd-snap case.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty vtd-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            #plotPoly(countyGeom[mC])\n",
    "            #plotPoly(stradPoly)\n",
    "            #for q in qtrList:\n",
    "            #    if not isContiguous(q,neighborList):\n",
    "            #        for v in q:\n",
    "            #            plotPoly(tractGeom[v])\n",
    "            #plt.show()\n",
    "            continue  #don't bother quartering up the muni-snap\n",
    "            \n",
    "        for i, HL in enumerate(halfList3):  #... and now, 4 qtr-districts for muni-snap\n",
    "            qCutPoint3  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            off2Pop3 = abs(0.5*halfPop3[i] - aDP)\n",
    "            TOL3 = max( 0.02 * aDP, toler - 0.5*off2Pop3)  #tighten if 2-district off\n",
    "            qtrCutNo ,maxQtrCutNo, cutGoodQtr = 0, 4, False\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            qLoopNo = 0\n",
    "            sliceAngle3 = vtdAngle[i]  #of four try angles, we start at the angle that worked for vtdSnap \n",
    "            while qLoopNo < maxQtrCutNo : #and not cutGoodQtr :\n",
    "                qAngle = sliceAngle3 + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo               \n",
    "                #Polly = angledPentaPoly(cutPoint3, qAngle, 5.*tractCP[mCfT].distance(cutPoint3) )\n",
    "                #if Polly.disjoint(tractCP[mCfT]):\n",
    "                #    qAngle += math.pi\n",
    "                qElist, qML, qtrPop3[qE], splitMuni = cutMuniSnap(0.5*halfPop3[i], TOL3, muniTractList,tractCP, HL,\n",
    "                                                                      muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "                popGap = 0.5*halfPop3[i] - qtrPop3[qE]\n",
    "                list3B, pop3B = list(),0.\n",
    "                if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "                    hT = getFarthestTract(cutPoint3, qElist, tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "                    list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,qML,\n",
    "                                                              muniPool3[i],tractCP, neighborList,tractPop3,0)\n",
    "                qElist, qtrPop3[qE] = qElist + list3B, qtrPop3[qE] + pop3B\n",
    "                qtrPop3[qO] = halfPop3[i] - qtrPop3[qE]\n",
    "                qOlist = list()\n",
    "                #print(\"t,i\",t,i, qtrPop[qE],qtrPop[qO],\"<-qpops, q3pops -->\",qtrPop3[qE],qtrPop3[qO])  #debug\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop3[qE] - aDP) <= 0.05*aDP and abs(qtrPop3[qO] - aDP) <= 0.05*aDP :\n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList3[qE], qtrList3[qO] = qElist.copy(), qOlist.copy()               \n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        #print(\"t,tryNo, final dPops for vtd- and muni\",t,nTries,qtrPop,qtrPop3)\n",
    "        if nGoodQtrCuts < 4:  #we failed to make four contiguous districts for both cases.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty muni-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            continue\n",
    "        \n",
    "        if soFarSoGood:\n",
    "            drawUnsuccessful = False\n",
    "            print(t,\"has drawn a GOOD 4-district Mtgmry for both vtd- and muni-snapped on try\",nTries)\n",
    "            for qD in range(4):\n",
    "                LISTS.append(qtrList[qD])\n",
    "                LISTS3.append(qtrList3[qD])\n",
    "    if not drawUnsuccessful:\n",
    "        Dayton7LISTS.append(LISTS)\n",
    "        Dayton7LISTS3.append(LISTS3)\n",
    "        Dayton7Starters.append(t)\n",
    "        if \"plot?\" == \"do not plot\": #alter to debug-plot\n",
    "            plotPoly(countyGeom[mC])\n",
    "            XX = [cutPoint.x - 0.1*math.cos(sliceAngle), cutPoint.x + 0.1*math.cos(sliceAngle)]\n",
    "            YY = [cutPoint.y - 0.1*math.sin(sliceAngle), cutPoint.y + 0.1*math.sin(sliceAngle)]\n",
    "            plt.plot(XX,YY)\n",
    "            for i,q in enumerate(qtrList):\n",
    "                dGeom = tractGeom[q[0]]\n",
    "                for v in q:\n",
    "                    dGeom = dGeom.union(tractGeom[v])\n",
    "                plotCenter(\"vtd\"+str(i),dGeom)\n",
    "                plotPoly(dGeom)\n",
    "            plt.show()\n",
    "            plotPoly(countyGeom[mC])\n",
    "            plotCenter(t,tractGeom[t])\n",
    "            XX = [cutPoint3.x - 0.1*math.cos(sliceAngle), cutPoint3.x + 0.1*math.cos(sliceAngle)]\n",
    "            YY = [cutPoint3.y - 0.1*math.sin(sliceAngle), cutPoint3.y + 0.1*math.sin(sliceAngle)]\n",
    "            plt.plot(XX,YY)\n",
    "            for i,q in enumerate(qtrList3):\n",
    "                dGeom = tractGeom[q[0]]\n",
    "                for v in q:\n",
    "                    dGeom = dGeom.union(tractGeom[v])\n",
    "                plotCenter(\"muni\"+str(i),dGeom)\n",
    "                plotPoly(dGeom)\n",
    "            plt.show()    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "11063538-55d9-45ae-90bb-544b8faad629",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40 39 n starter tracts, n completed Dayton plans\n",
      "there are 1223 tracts in the counties surrounding Dayton\n",
      "40 39 39 nStarters, nLists, nLists3\n"
     ]
    }
   ],
   "source": [
    "print(len(countyStraddlerList[56]),len(Dayton7Starters), \"n starter tracts, n completed Dayton plans\")\n",
    "nDaytonTracts = 0\n",
    "for c in DaytonList:\n",
    "    nDaytonTracts += len(countyTractList[c])\n",
    "print(\"there are\",nDaytonTracts,\"tracts in the counties surrounding Dayton\")\n",
    "print(len(countyStraddlerList[mC]), len(Dayton7LISTS), len(Dayton7LISTS3),\"nStarters, nLists, nLists3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "b9976c17-4503-4cf9-94aa-72fceb843747",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time for Dayton-area tracts to vote.  Must first classify Greene and Clark as voters or non\n",
      "Voting done.  The most popular plan was 30 with 0.096 of the vote\n",
      "Voting done.  The most popular plan was 4 with 0.149 of the vote\n",
      "we found 791 muni voting tracts.  Max possible = 1027\n",
      "let us write the HD weights to a file for sub-region Dayton Dayton\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(\"time for Dayton-area tracts to vote.  Must first classify Greene and Clark as voters or non\" ) \n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "fName = \"Dayton\" \n",
    "areaVoters = [countyTractList[mC], countyTractList[mC] ] \n",
    "rLISTS = [Dayton7LISTS.copy(), Dayton7LISTS3.copy() ]\n",
    "nPlans = [len(rLISTS[0]), len(rLISTS[1]) ]\n",
    "for i, rList in enumerate(rLISTS):\n",
    "    nDs_perPlan = len(rList[0])  #i.e. 7 districts\n",
    "    nTotalDs = nDs_perPlan * nPlans[i]\n",
    "    dTL_CP = [ [Point(0,0)]*nTotalDs for p in range(nPlans[i])]  #centerpoint of each district in each plan\n",
    "\n",
    "    for planNo, PLAN in enumerate(rList):  #define all plan centerpoints for later distance assessment\n",
    "        for dNo in range(nDs_perPlan):\n",
    "            dTL_CP[planNo][dNo] = get_PCP(rList[planNo][dNo] ,tractCP, tractPop3)\n",
    "\n",
    "    for pC in [pairedCountyList[0], pairedCountyList[1]] :  #Outside Mtgmry, only Greene and Clark can vote if better-ctrd\n",
    "        for v in countyTractList[pC]:\n",
    "            nextbestDistance = countyPCP[pC].distance(tractCP[v])\n",
    "            foundBetterPlan = False\n",
    "            for planNo,PLAN in enumerate(rList):\n",
    "                for dNo, districtList in enumerate(PLAN):\n",
    "                    if v in districtList and not foundBetterPlan:  #just need to find one\n",
    "                        if dTL_CP[planNo][dNo].distance(tractCP[v]) < nextbestDistance:\n",
    "                            areaVoters[i].append(v)\n",
    "                            foundBetterPlan = True\n",
    "                            break\n",
    "\n",
    "rWeights =  [setPlanWeights(areaVoters[0], rLISTS[0], tractCP, tractPop3), \n",
    "             setPlanWeights(areaVoters[1], rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"we found\",len(areaVoters[1]),\"muni voting tracts.  Max possible =\",\n",
    "      len(countyTractList[mC]+countyTractList[pairedCountyList[0]]+countyTractList[pairedCountyList[1]] ) )\n",
    "print(\"let us write the HD weights to a file for sub-region\",fName,regionName[currentRegionNo])\n",
    "date = \"05May\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "45ddfb51-c957-4a1a-ac1d-aa8c8a8e3887",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing the vote distro (vtd, then muni)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing the vote distro (muni)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing the vote distro (vtd, then muni)\")\n",
    "for p,t in enumerate(Dayton7Starters):\n",
    "    if rWeights[0][p] > 0.01:\n",
    "        plotCenter(r3(rWeights[0][p]),tractCP[t] )\n",
    "plotPoly(countyGeom[mC])\n",
    "plotCenter(\"vtd\",countyGeom[mC])\n",
    "plt.show()\n",
    "print(\"visualizing the vote distro (muni)\")\n",
    "for p,t in enumerate(Dayton7Starters):\n",
    "    if rWeights[1][p] > 0.01:\n",
    "        plotCenter(r3(rWeights[1][p]),tractCP[t] )\n",
    "plotPoly(countyGeom[mC])\n",
    "plotCenter(\"muni\",countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "49a65e49-8316-4bd7-8d2b-873991892fc0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On to Toledo area\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#WIP 3/11/23\n",
    "print(\"On to Toledo area\")\n",
    "mC = 47 #main county = Lucas'\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "#currentRegionNo = 0   #\"NW\"\n",
    "pairedCountyList = [86, 25, 61]  #Wood, Fulton, Ottawa\n",
    "altPairingList = [ [], [34, 85], [71] ]  #these are the counties the paired counties would link with if not drawn into a straddler \n",
    "ppC = -999  #a default flag to not draw a whole district in any neighboring counties\n",
    "ppC = pairedCountyList[0]  #primary paired county (e.g. Wood = 86 - we choose to include a Wood partial in ALL our reg'l plans\n",
    "luringCountyList = [61, 34] #luring HDPoly's toward counties adjoining southwest and east corners of Lucas\n",
    "forcedTractList = [3115, 3068]  #for Lucas, we force all straddler HDPoly to have included either the SW or SE corner tract\n",
    "ppCmaxPop = countyPop[ppC] % avgDistrictPop  #the max population from the primary pairing county that can be used in a straddling district\n",
    "targetRemnantPop = avgDistrictPop * (countyDistricts[mC]%1.)  #the natural pop left after filling whole districts in main county\n",
    "luringList = []\n",
    "for c in luringCountyList:\n",
    "    for tt in countyTractList[c]:\n",
    "        luringList.append(tt)   #these tracts in nearby counties \"lure\" HDs which contain them to center straddler HDs\n",
    "forcedGeom = countyGeom[mC]   #default; we do not force any tracts to be in the original HDPoly's of straddler districts\n",
    "if len(forcedTractList) > 0:\n",
    "    forcedGeom = tractCP[forcedTractList[0]]  #NOT THE WHOLE MAIN COUNTY \n",
    "    for T in forcedTractList:\n",
    "        forcedGeom = forcedGeom.union(tractCP[T])\n",
    "countyStraddlerList[mC] = get_straddlers(targetRemnantPop, countyPop[mC], countyTractList[mC], luringList, forcedGeom,\n",
    "                                         HDPoly, tractCP, tractPop3)\n",
    "mC_EnsnaredPop = 0.\n",
    "for t in countyTractList[mC]:\n",
    "    if t in countyStraddlerList[mC]:\n",
    "        HDtype[t] = \"straddlerMaker\"        \n",
    "        plt.scatter(HDCPx[t],HDCPy[t],marker=\"x\", label=\"generated a straddler HD\")\n",
    "        mC_EnsnaredPop += tractPop3[t]\n",
    "    else:\n",
    "        HDtype[t] = \"notAstraddlerMaker\"\n",
    "        plt.scatter(HDCPx[t],HDCPy[t],marker=\".\", label=\"not a straddler generator\")\n",
    "plotPoly(countyGeom[mC])\n",
    "for t in forcedTractList:\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7cbd0d0c-07a3-48bc-8649-7c291036a0cf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53\n",
      "here are your 47 county muni's > 10K pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(len(countyStraddlerList[mC]))\n",
    "print(\"here are your\",mC,\"county muni's > 10K pop\")  #Sylvania is surrounded by Sylv Township, so we bundle together here\n",
    "for m in countyMuniList[mC]:\n",
    "    if muniPop[m] > 10000:\n",
    "        plotPoly(muniGeom[m])\n",
    "        plotCenter(muniPop[m],muniGeom[m])\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "df1f64b4-a3f7-4f6e-91b3-2cf0157d3ddd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now build the HDtractList (and the resulting regional plan) for each possible straddler HD spawned in county 47\n",
      "3102 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3098 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2939 3 6949.0 True t,i,vtd-pop,contiguity = FAIL\n",
      "2939 4 232082.0 True t,i,vtd-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 5 of 5 legal districts for vtd-, muni-snap for vtd 2939\n",
      "nil 86 county intersxn for vtd 3101\n",
      "3101 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3101 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "working on tract 3100\n",
      "3100 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3096 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3068\n",
      "3068 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3068 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3097\n",
      "3097 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3097 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3095 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3069 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3227\n",
      "3227 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3227 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3158\n",
      "3158 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3158 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3115 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2991 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2993 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2973 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3155\n",
      "3155 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3155 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3153\n",
      "3153 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3153 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3099 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3121 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3004\n",
      "3004 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3004 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2972 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3145 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3208 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3012\n",
      "3012 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3012 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3085 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3009 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3157 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2997 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3199\n",
      "3199 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3199 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3170 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3047\n",
      "3047 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3047 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3114 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "failed wholeD Wood pop or contig from starter 3083 . vtd, muni pops were 123248.0 123078.0\n",
      "Boo! We only generated 4 4 of 5 legal districts for vtd-, muni-snap for vtd 3083\n",
      "2985 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "failed wholeD Wood pop or contig from starter 2984 . vtd, muni pops were 123248.0 123078.0\n",
      "Boo! We only generated 4 4 of 5 legal districts for vtd-, muni-snap for vtd 2984\n",
      "3028 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 2957\n",
      "2957 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "2957 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3127 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3071 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3138\n",
      "3138 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3138 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2950 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3084 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3159\n",
      "3159 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3159 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3154 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2996 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2971 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 2954\n",
      "2954 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "2954 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 2961\n",
      "2961 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "2961 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "nil 86 county intersxn for vtd 3144\n",
      "3144 's target ICP is now 78822.34343434343 cuz ppC 61 can only contribute 40364.0\n",
      "3144 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "2983 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3075 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "3074 has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\n",
      "all done creating regional plans for region number 0  =  NW\n",
      "we had a total of 50 jiggy regional plans, max = 53\n"
     ]
    }
   ],
   "source": [
    "# 3/11/23 - modifying for muni-snap, reapply from Dayton. \n",
    "#3/12/23 29/53 Jiggy. Having vtd-snap check districts 0 & 1 for discontig, if so, reapply munisnap?\n",
    "pairedCountyList = [86, 25, 61]  #Wood, Fulton, Ottawa\n",
    "altPairingList = [ [], [34, 85], [71] ]  #these are the counties the paired counties would link with if not drawn into a straddler \n",
    "Toledo5LISTS, Toledo5LISTS3, Toledo5Starters = list(), list(), list()\n",
    "toler = 0.05*aDP\n",
    "print(\"I will now build the HDtractList (and the resulting regional plan) for each possible straddler HD spawned in county\",mC)\n",
    "nu = random.randint(len(countyStraddlerList[mC])-10)\n",
    "for t in countyStraddlerList[mC]: #for Lucas, the HDpop comes from mC=Lucas, then ppC=Wood, then a mop-up county\n",
    "    if t%20 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    nSplits = 0   \n",
    "    nGoodDistricts = 0\n",
    "    targetICP = countyPop[mC]%aDP #target in-county pop for a straddler district.  Reset if prev loop used a whole ppC\n",
    "    HDangle = [HDangle0[t],  HDangle1[t], HDangle2[t], HDangle3[t] ]\n",
    "    HDradius = [HDradius0[t], HDradius1[t], HDradius2[t], HDradius3[t] ]\n",
    "    HDtractList[t] = []\n",
    "    adjCountyHDPolyPop = [0.] * nCounties  #picking the spC based on pop in original HDpoly\n",
    "    ppC = pairedCountyList[0]  #USUALLY Wood for Lucas, but we reject Wood if no intersection w orig HDPoly    \n",
    "    intersectsPCL0 = False  #i.e. Wood County\n",
    "    for v in countyTractList[ppC]:\n",
    "        if HDPoly[t].intersects(tractCP[v]):  #must intersect a vtd center for HDvtdSnap to work\n",
    "            intersectsPCL0 = True\n",
    "            break\n",
    "    if intersectsPCL0:     #assign the secondary pairing county            \n",
    "        for cc in pairedCountyList:  #see Dayton area for a more complicated spC selection.  For Toledo, only 2 choices\n",
    "            if cc != ppC:\n",
    "                for tt in countyTractList[cc]:\n",
    "                    overlap = HDPoly[t].intersection(tractGeom[tt]).area\n",
    "                    if overlap > 0:\n",
    "                        adjCountyHDPolyPop[cc] += tractPop[tt] * overlap / tractGeom[tt].area\n",
    "        if np.sum(adjCountyHDPolyPop) == 0:  #(rare) orig HDPoly contained in mC+ ppC\n",
    "            cDist = [countyGeom[pairedCountyList[i]].distance(tractCP[t]) for i in range(1,len(pairedCountyList)) ]\n",
    "            spC = pairedCountyList[cDist.index(np.min(cDist))]\n",
    "            print(\"assigning\",spC,\"as 2ary pairing county as no candidates intersected HDPoly[\",t,\"]\")\n",
    "        else:\n",
    "            spC = adjCountyHDPolyPop.index(np.max(adjCountyHDPolyPop) ) #the 2ary pairing county had the most pop in this t's orig HD\n",
    "    else:\n",
    "        spC = pairedCountyList[0]  #always Wood \n",
    "        print(\"nil\",pairedCountyList[0],\"county intersxn for vtd\",t)\n",
    "        ppC = pairedCountyList[1]  #can't pick Wood.  Iterate thru other choices\n",
    "        ppCarea = countyGeom[ppC].intersection(HDPoly[t]).area\n",
    "        for c in pairedCountyList[2:] :\n",
    "            if countyGeom[c].intersection(HDPoly[t]).area > ppCarea:\n",
    "                ppC = c\n",
    "                ppCarea = countyGeom[c].intersection(HDPoly[t]).area\n",
    "        if countyPop[ppC] < 1.05*aDP - targetICP and countyPop[ppC] > 0.95*aDP - targetICP :\n",
    "            targetICP = aDP - countyPop[ppC]  #Fulton and Ottawa are just under a perfect pairing to Lucas partial\n",
    "            print(t,\"'s target ICP is now\",targetICP,\"cuz ppC\",ppC,\"can only contribute\",countyPop[ppC])\n",
    "                \n",
    "    toler2 = toler*0.7  #reduce each sub-district's slop to reduce error propagation\n",
    "    cutPt =countyPCP[mC]\n",
    "    stdSliceAngle = 0.5 * math.pi #vertical, default    \n",
    "    if angledPentaPoly(cutPt,stdSliceAngle,5.*countyGeom[mC].area).disjoint(tractCP[t]):\n",
    "        stdSliceAngle += math.pi \n",
    "    localSliceAngle = getSliceAngle(tractCP[t], cutPt, tractCP[t])\n",
    "    sliceAngle = 0.6*stdSliceAngle + (1. - 0.6)*localSliceAngle  #shade toward more compact shape\n",
    "    if tractCP[t].y > 41.68:  #we have problems with north Toledo starters\n",
    "        sliceAngle = 0.8 * math.pi  #northernmost \"straddlers\" need to avoid central Toledo\n",
    "    if angledPentaPoly(cutPt,sliceAngle,5.*countyGeom[mC].area).disjoint(tractCP[t]):\n",
    "        sliceAngle += math.pi     \n",
    "    mClist, mCsnapPop = cutVtdSnap(targetICP, toler2, tractCP, countyTractList[mC], neighborList, tractPop3, sliceAngle, cutPt,t)\n",
    "    mClist3, mcML3, mCsnapPop3, splitMuni = cutMuniSnap(targetICP, toler2, muniTractList,tractCP, countyTractList[mC],\n",
    "                                                        muniNeighborList,tractPop3, muniPop, muniPCP, sliceAngle, cutPt)\n",
    "    #mClist3, mcML3, mCsnapPop3, splitMuni = HDmuniSnap(t,targetICP, toler2, muniTractList, tractCP,countyMuniList[mC],\n",
    "    #                                                     muniNeighborList,tractPop3, muniPop, muniPCP, HDangle, HDradius)\n",
    "    popGap = targetICP - mCsnapPop3\n",
    "    mClist3B, mCsnapPop3B = list(),0.\n",
    "    if (abs(popGap) > toler) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "        if popGap < 0  :\n",
    "            raise Exception(\"error! mCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            \n",
    "        mClist3B, mCsnapPop3B = getFillList3noSplit(t, popGap, toler2, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                    mcML3, countyMuniList[mC],tractCP, neighborList,tractPop3)\n",
    "        if mCsnapPop3B == 0. :\n",
    "            print(\"uh oh, failed to create whole-muni in inCounty part of straddler district for tract\",t,\"we are short by\",popGap)\n",
    "        \n",
    "        mClist3, mCsnapPop3 = mClist3 + mClist3B, mCsnapPop3 + mCsnapPop3B  #completing in the inCounty partial despite splitM flag\n",
    "    \n",
    "    stradList, stradList3 = mClist.copy(), mClist3.copy()\n",
    "    stradPop,stradPop3  = mCsnapPop, mCsnapPop3\n",
    "    #print(\"t,mCsnapPop, mCsnapPop3 are\",t,mCsnapPop, mCsnapPop3,\"vs tgt\",targetICP)\n",
    "    isFullppC, isFullppC3 = False, False  #note for Lucas: Wood usually ppC, but sometimes we take a full other cty\n",
    "    if countyPop[ppC] > aDP : #pairing county constrained, nontaken remnant must form whole district(s)\n",
    "        ppCtgtPop, ppCtgtPop3 = countyPop[ppC]%avgDistrictPop, countyPop[ppC]%avgDistrictPop\n",
    "    else:  #HERE, WE CURRENTLY ASSUME NO PARTIAL COUNTIES BIGGER THAN NEEDED TO FILL.  REVISE FOR NE OHIO\n",
    "        #add the whole county; it'll fit (provided we excluded non-legit pairing counties from pClist)\n",
    "        ppCtgtPop, ppCtgtPop3 = aDP - mCsnapPop, aDP - mCsnapPop3\n",
    "        if countyPop[ppC] < ppCtgtPop + toler and countyPop[ppC] > ppCtgtPop - toler :\n",
    "            #print(\"we will complete vtd straddler for t\",t,\"by adding all of county\",ppC)\n",
    "            ppClist, ppCsnapPop, isFullppC, spC = countyTractList[ppC], countyPop[ppC], True, -999\n",
    "            if mCsnapPop + countyPop[ppC] < aDP - toler:\n",
    "                print(\"OOPS! adding full county\",ppC,countyPop[ppC],\"and we still need\",int(aDP-mCsnapPop-countyPop[ppC]) )\n",
    "        if countyPop[ppC] < ppCtgtPop3 + toler and countyPop[ppC] > ppCtgtPop3 - toler:\n",
    "            #print(\"we will complete muni straddler for t\",t,\"by adding all of county\",ppC)\n",
    "            ppClist3, ppCsnapPop3, isFullppC3, spC = countyTractList[ppC], countyPop[ppC], True, -999       \n",
    "            \n",
    "    if not isFullppC:\n",
    "        ppClist, ppCsnapPop = vtdSwell(tractCP[t], ppCtgtPop, toler2, tractCP, countyTractList[ppC],neighborList,tractPop3,1.) \n",
    "\n",
    "    if not isFullppC3:\n",
    "        ppClist3, ppCsnapPop3, ppCsplits = muniSwell(tractCP[t], ppCtgtPop3, toler2, aDP, muniPop, muniPCP, muniTractList,\n",
    "                                                     muniNeighborList,countyMuniList[ppC], tractCP, neighborList, tractPop3, 0, 1.) \n",
    "        #print(t,ppCsnapPop, ppCsnapPop3,\"ppC pops vs tgts\",int(ppCtgtPop), int(ppCtgtPop3))\n",
    "    \n",
    "    stradList  += ppClist\n",
    "    stradList3 += ppClist3\n",
    "    stradPop  += ppCsnapPop\n",
    "    stradPop3 += ppCsnapPop3 \n",
    "    #print(\"after adding ppC\",ppC,\"t, HDpop and pop3 are\",t, stradPop, stradPop3)\n",
    "         \n",
    "    spClist, spClist3, spCsnapPop, spCsnapPop3 = [],[],0,0\n",
    "    spCtgtPop = avgDistrictPop - stradPop  \n",
    "    if spCtgtPop > 0.05 * avgDistrictPop: #district not yet complete; add the mopup\n",
    "        spClist, spCsnapPop = vtdSwell(tractCP[t], spCtgtPop, toler2, tractCP, countyTractList[spC],neighborList,tractPop3,1.) \n",
    "\n",
    "    spCtgtPop3 = avgDistrictPop - stradPop3\n",
    "    if spCtgtPop3 > 0.05 * avgDistrictPop :         \n",
    "        spClist3, spCsnapPop3, spCsplits = muniSwell(tractCP[t], spCtgtPop3, toler2, aDP, muniPop, muniPCP, muniTractList,\n",
    "                                                     muniNeighborList,countyMuniList[spC], tractCP, neighborList, tractPop3,\n",
    "                                                     ppCsplits, 1.)             \n",
    "    stradList  += spClist\n",
    "    stradList3 += spClist3\n",
    "    stradPop  += spCsnapPop\n",
    "    stradPop3 += spCsnapPop3\n",
    "    if abs(stradPop - aDP) <= toler and abs(stradPop3 - aDP) <= toler:\n",
    "        #print(t,\" 's pops are good\")\n",
    "        if(isContiguous(stradList, neighborList) and isContiguous(stradList3, neighborList) ):\n",
    "            #print(t,\" 's districts are contiguous\")\n",
    "            nGoodDistricts +=1\n",
    "    if nGoodDistricts == 0:\n",
    "        print(\"failed straddler pop or contig from starter\",t,\". vtd, muni pops were\",stradPop, stradPop3)\n",
    "        continue #no point trying to build rest of 5-district region\n",
    "    LISTS, LISTS3  = [stradList],[stradList3]\n",
    "    LISTpops, LISTpops3 =  [stradPop], [ stradPop3 ]\n",
    "    #    *****************2nd district is the remainder of large potential ppC's or a whole district in them if not used yet\n",
    "    bigClist = list()\n",
    "    for c in pairedCountyList:\n",
    "        if countyPop[c] > 1.05*aDP and countyPop[c] < 1.95 * aDP:\n",
    "            bigClist.append(c)  #only Wood for Toledo area\n",
    "    isWCwhole = [\"yes\"]*len(bigClist) \n",
    "    #print(t,ppC,ppCsnapPop, ppCsnapPop3,spC,spCsnapPop,spCsnapPop3,\"t,ppC & pops,spC&pops\")\n",
    "    for i,wC in enumerate(bigClist):\n",
    "        wClist, wClist3, wCPop, wCPop3 = list(), list(), 0., 0.\n",
    "        if ppC == wC or spC == wC:  #should always be true for Toledo area -- Wood partial forced into straddler\n",
    "            isWCwhole[i] = \"no\"\n",
    "        if isWCwhole[i] == \"no\":  #magic partial already drawn into straddler.  Remainder of this county forms whole district\n",
    "            for tt in countyTractList[wC]:\n",
    "                if tt not in stradList:\n",
    "                    wClist.append(tt)\n",
    "                    wCPop += tractPop3[tt]\n",
    "                if tt not in stradList3:\n",
    "                    wClist3.append(tt)\n",
    "                    wCPop3 += tractPop3[tt]\n",
    "        else:  #grow a whole district via pizza cut.  ID the \"starter\" tract closest to the HDCP to orient the cut (new 3/23)\n",
    "            wCpool = countyTractList[wC]           \n",
    "            cutPoint = countyPCP[wC]\n",
    "            wCcT = getClosestTract(tractCP[t], wCpool, tractCP)  #wholeCounty tract closest to the HD-centering tract\n",
    "            sliceAngle = getSliceAngle(tractCP[t], cutPoint, tractCP[wCcT])\n",
    "            wClist, wCPop = cutVtdSnap(aDP, toler, tractCP, wCpool, neighborList, tractPop3, sliceAngle, cutPoint,wCcT)\n",
    "            wClist3, wCML3, wCPop3, splitMuni = cutMuniSnap(aDP, toler, muniTractList,tractCP, wCpool, muniNeighborList,\n",
    "                                                               tractPop3, muniPop, muniPCP, sliceAngle, cutPoint)\n",
    "            popGap = aDP - wCPop3\n",
    "            if popGap > toler:  #needed to split a muni, but didn't\n",
    "                wClist3B, wCPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,wCML3,\n",
    "                                                        countyMuniList[wC],tractCP, neighborList,tractPop3,nSplits)        \n",
    "                wClist3, wCPop3 = wClist3 + wClist3B, wCPop3 + wCPop3B\n",
    "        if abs(wCPop - aDP) <= toler and abs(wCPop3 - aDP) <= toler:\n",
    "            if(isContiguous(wClist, neighborList) and isContiguous(wClist3,neighborList) ):\n",
    "                nGoodDistricts +=1\n",
    "        if nGoodDistricts == 1:\n",
    "            print(\"failed wholeD Wood pop or contig from starter\",t,\". vtd, muni pops were\",wCPop, wCPop3)\n",
    "            continue #no point trying to build rest of 5-district region\n",
    "        LISTS.append(wClist)\n",
    "        LISTS3.append(wClist3)   \n",
    "        LISTpops.append(wCPop)\n",
    "        LISTpops3.append(wCPop3)\n",
    "\n",
    "    # now for each straddler tract, slice up the remaining whole county into 3 parallel slices, for others to vote on LATER\n",
    "    nonstraddlerList, nonstraddlerList3, muni3dPool3 = list(), list(), list()  #analogous to mCpool\n",
    "    for v in countyTractList[mC]:\n",
    "        if v not in stradList:  #i.e. not in this proposed straddler district\n",
    "            nonstraddlerList.append(v)\n",
    "        if v not in stradList3:  #i.e. not in this proposed straddler district\n",
    "            nonstraddlerList3.append(v)\n",
    "            if tractMuniNo[v] not in muni3dPool3:\n",
    "                muni3dPool3.append(tractMuniNo[v])\n",
    "    stdANGLE = 0.5 * math.pi  #for Lucas county, a vertical cut is reasonable\n",
    "    nonstraddlerPCP = get_PCP(nonstraddlerList,tractCP, tractPop3)  #... uncomment below to influence on ideal cut angle\n",
    "    #dx = tractCP[t].x - nonstraddlerPCP.x \n",
    "    #dy = tractCP[t].y - nonstraddlerPCP.y\n",
    "    #radialAngle = math.pi/2. * np.sign(dy)\n",
    "    #if dx != 0 :\n",
    "    #    radialAngle = math.atan(dy/dx)  #will be -0.5 pi to 0.5 pi\n",
    "    cutAngle = 0.5* math.pi + random.uniform(-0.1*math.pi, 0.1*math.pi)  #add a little variation\n",
    "    sliceAngle = cutAngle #0.5*(stdANGLE + cutAngle)  #SKIP mild accommodation for shape of nonstraddler group\n",
    "    cT = getClosestTract(tractCP[t], nonstraddlerList, tractCP)\n",
    "    if angledPentaPoly(nonstraddlerPCP,sliceAngle,5.*countyGeom[mC].area).disjoint(tractCP[cT]):\n",
    "        sliceAngle += math.pi\n",
    "    \n",
    "    #do we want to try alt angles?  Let's see how vertical does\n",
    "    sliceAngle3 = sliceAngle\n",
    "    triList, triList3, triPop, triPop3 = [list(), list(), list()], [list(), list(), list()], [0.]*3, [0.]*3\n",
    "    tgtTriPop  = (countyPop[mC]  - mCsnapPop)  / int(countyDistricts[mC])\n",
    "    tgtTriPop3 = (countyPop[mC]  - mCsnapPop3) / int(countyDistricts[mC])\n",
    "    tol2, tol3 = 0.03 * aDP, 0.03*aDP  #constrain a little\n",
    "    mCpool, mCpool3 = nonstraddlerList.copy(), nonstraddlerList3.copy()  #nomenclature   \n",
    "    #cutPoint = get_PCP(mCpool, tractCP, tractPop3) #pop center of the mC's tracts in the 3 whole districts   \n",
    "    #cutPoint3 = get_PCP(mCpool3, tractCP, tractPop3) #pop center of the mC's tracts in the 3 whole districts\n",
    "    cutPoint, cutPoint3 = countyPCP[mC], countyPCP[mC] #should be about right for 3.5 district Lucas\n",
    "    triList[0], triPop[0] = cutVtdSnap(tgtTriPop, tol2, tractCP, mCpool, neighborList, tractPop3, sliceAngle, cutPoint)\n",
    "    triList3[0], hML, triPop3[0], splitMuni = cutMuniSnap(tgtTriPop3, tol3, muniTractList,tractCP, mCpool3, muniNeighborList, \n",
    "                                                            tractPop3, muniPop, muniPCP, sliceAngle, cutPoint3)\n",
    "    popGap, fillPop = tgtTriPop3 - halfPop3[0], 0.\n",
    "    if abs(popGap) > tol3 :  #try alternate muni's, do not allow a split at this stage\n",
    "        #print(t,nTries,len(halfList3[0]),halfPop3[0], popGap, int(tol3),\"t,nTries, halfList3[0] len, pop & its popGap, tol3 prior to gFL3nS attempt\")\n",
    "        hT = getFarthestTract(cutPoint3, triList3[0], tractCP)  #\"home\" tract to bias fill-in toward compactness\n",
    "        vlist, fillPop = getFillList3noSplit(hT, popGap, tol3,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,hML,\n",
    "                                             muni3dPool3, tractCP, neighborList, tractPop3)\n",
    "        triList3[0] += vlist\n",
    "        triPop3[0]  += fillPop\n",
    "    doubleList, doubleList3, doublePop, doublePop3 = list(), list(), 0., 0.\n",
    "    for v in mCpool:\n",
    "        if v not in triList[0]:\n",
    "            doubleList.append(v)\n",
    "            doublePop  += tractPop3[v]\n",
    "    muniPool3 = list()  #the list of munis in remaining 2-district set (we did not allow splitMuni in prev cut)\n",
    "    for v in mCpool3:\n",
    "        if v not in triList3[0]:\n",
    "            doubleList3.append(v)\n",
    "            doublePop3 += tractPop3[v]\n",
    "            if tractMuniNo[v] not in muniPool3:\n",
    "                muniPool3.append(tractMuniNo[v])\n",
    "\n",
    "    #for i, HL in enumerate(halfList):  #revised Dayton code -- here, only one more cut to make, not 2 halves\n",
    "    HL = doubleList.copy()  #reusing Dayton nomenclature\n",
    "    off2Pop = abs(0.5*doublePop - aDP)\n",
    "    TOL = 0.05*aDP - off2Pop  #tighten if 2-district off\n",
    "    qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "    qAngle = sliceAngle  #KISS - don't change sliceAngle for second cut\n",
    "    triList[1], triPop[1] = cutVtdSnap(0.5*doublePop, TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)\n",
    "    for tt in HL:\n",
    "        if tt not in triList[1]:  #throw all remaining vtds into 3rd whole Lucas district\n",
    "            triList[2].append(tt)\n",
    "    triPop[2] = doublePop - triPop[1]\n",
    "\n",
    "    HL3 = doubleList3.copy()  #reusing Dayton code\n",
    "    qCutPoint3 = get_PCP(HL3, tractCP, tractPop3)  #note: reusing same angle (vertical)\n",
    "    off2Pop3 = abs(0.5*doublePop3 - aDP)\n",
    "    TOL3 = 0.05*aDP - off2Pop3  #tighten if 2-district off\n",
    "    triList3[1], tML, triPop3[1], splitMuni = cutMuniSnap(0.5*doublePop3, TOL3, muniTractList,tractCP, HL3,\n",
    "                                                          muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "    popGap = 0.5*doublePop3 - triPop3[1]\n",
    "    list3B, pop3B = list(),0.\n",
    "    if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "        hT = getFarthestTract(cutPoint3, triList3[1], tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "        list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,tML,\n",
    "                                                  muniPool3,tractCP, neighborList,tractPop3,0)\n",
    "    triList3[1], triPop3[1] = triList3[1] + list3B, triPop3[1] + pop3B\n",
    "    for tt in HL3:\n",
    "        if tt not in triList3[1]:  #throw all remaining vtds into 3rd whole Lucas district\n",
    "            triList3[2].append(tt)\n",
    "    triPop3[2] = doublePop3 - triPop3[1]\n",
    "\n",
    "    for i, L in enumerate(triList3):\n",
    "        LISTS.append(triList[i])\n",
    "        LISTS3.append(triList3[i])\n",
    "        LISTpops.append(triPop[i])\n",
    "        LISTpops3.append(triPop3[i])\n",
    "    nGood, nGood3 = 0,0\n",
    "    for i, L in enumerate(LISTS):\n",
    "        if abs(LISTpops[i] - aDP) <= 0.05 * aDP and isContiguous(LISTS[i],neighborList):\n",
    "            nGood +=1\n",
    "        else:\n",
    "            print(t,i,LISTpops[i],isContiguous(LISTS[i],neighborList),\"t,i,vtd-pop,contiguity = FAIL\")\n",
    "        if abs(LISTpops3[i] - aDP) <= 0.05 * aDP and isContiguous(LISTS3[i],neighborList):\n",
    "            nGood3 +=1\n",
    "        else:\n",
    "            print(t,i,LISTpops3[i],isContiguous(LISTS3[i],neighborList),\"t,i,muni-pop,contiguity = FAIL\")\n",
    "            \n",
    "    if nGood == 5 and nGood3 == 5:\n",
    "        drawUnsuccessful = False\n",
    "        print(t,\"has drawn a GOOD straddler + Wood + 3-district Toledo; add to growing list\")       \n",
    "        Toledo5LISTS.append(LISTS)\n",
    "        Toledo5LISTS3.append(LISTS3)\n",
    "        Toledo5Starters.append(t)\n",
    "    else:\n",
    "        print(\"Boo! We only generated\",nGood, nGood3,\"of 5 legal districts for vtd-, muni-snap for vtd\",t )\n",
    "print(\"all done creating regional plans for region number\",currentRegionNo,\" = \",regionName[currentRegionNo])\n",
    "print(\"we had a total of\",len(Toledo5Starters),\"jiggy regional plans, max =\",len(countyStraddlerList[mC]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "eaa82d6d-6ba6-4729-9f13-f63ceba5fff9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing 3 of 5 Toledo districts for a random set; there are 50 to choose from\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing 3 of 5 Toledo districts for a random set; there are\",len(Toledo5LISTS),\"to choose from\")\n",
    "n = random.randint(len(Toledo5LISTS3))\n",
    "for v in Toledo5LISTS3[n][0]:\n",
    "    plotPoly(tractGeom[v])\n",
    "for v in Toledo5LISTS3[n][1]:\n",
    "    plotPoly(tractCP[v].buffer(0.002))\n",
    "for v in Toledo5LISTS3[n][3]:\n",
    "    plotPoly(tractCP[v].buffer(0.004))\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyGeom[86]) #Wood\n",
    "plotCenter(n,countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "7acdd26a-d4d0-4773-9f66-61e2f47f5170",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time for Toledo-area tracts to vote.  Must first classify Ottawa and Fulton as voters or non\n",
      "Voting done.  The most popular plan was 43 with 0.084 of the vote\n",
      "Voting done.  The most popular plan was 16 with 0.121 of the vote\n",
      "we found 431 muni voting tracts.  Max possible = 472\n",
      "let us write the HD weights to a file for sub-region Toledo NW\n"
     ]
    }
   ],
   "source": [
    "print(\"time for Toledo-area tracts to vote.  Must first classify Ottawa and Fulton as voters or non\" ) \n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "fName = \"Toledo\" \n",
    "areaVoters = [countyTractList[mC]+countyTractList[pairedCountyList[0]], countyTractList[mC]+countyTractList[pairedCountyList[0]] ] \n",
    "rLISTS = [Toledo5LISTS.copy(), Toledo5LISTS3.copy() ]\n",
    "nPlans = [len(rLISTS[0]), len(rLISTS[1]) ]\n",
    "for i, rList in enumerate(rLISTS):\n",
    "    nDs_perPlan = len(rList[0])  #i.e. 7 districts\n",
    "    nTotalDs = nDs_perPlan * nPlans[i]\n",
    "    dTL_CP = [ [Point(0,0)]*nTotalDs for p in range(nPlans[i])]  #centerpoint of each district in each plan\n",
    "\n",
    "    for planNo, PLAN in enumerate(rList):  #define all plan centerpoints for later distance assessment\n",
    "        for dNo in range(nDs_perPlan):\n",
    "            dTL_CP[planNo][dNo] = get_PCP(rList[planNo][dNo] ,tractCP, tractPop3)\n",
    "\n",
    "    for iii,pC in enumerate([pairedCountyList[1], pairedCountyList[2]]) :  #Woods always vote, but not all Ottawa and Fulton\n",
    "        for v in countyTractList[pC]:\n",
    "            altCs = altPairingList[iii+1]  #\"+1\" because above loop skips Wood\n",
    "            altCTL = countyTractList[pC].copy()\n",
    "            for altC in altCs:\n",
    "                altCTL += countyTractList[altC]  #the alt district for this pC includes alt neighboring county tracts\n",
    "            nextbestDistance = get_PCP(altCTL, tractCP, tractPop3).distance(tractCP[v])\n",
    "            foundBetterPlan = False\n",
    "            for planNo,PLAN in enumerate(rList):\n",
    "                for dNo, districtList in enumerate(PLAN):\n",
    "                    if v in districtList and not foundBetterPlan:  #just need to find one\n",
    "                        if dTL_CP[planNo][dNo].distance(tractCP[v]) < nextbestDistance:\n",
    "                            areaVoters[i].append(v)\n",
    "                            foundBetterPlan = True\n",
    "                            break\n",
    "\n",
    "rWeights =  [setPlanWeights(areaVoters[0], rLISTS[0], tractCP, tractPop3), \n",
    "             setPlanWeights(areaVoters[1], rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"we found\",len(areaVoters[1]),\"muni voting tracts.  Max possible =\",\n",
    "      len(countyTractList[mC]+countyTractList[pairedCountyList[0]]+countyTractList[pairedCountyList[1]] +\n",
    "         countyTractList[pairedCountyList[2]] ) )\n",
    "print(\"let us write the HD weights to a file for sub-region\",fName,regionName[currentRegionNo])\n",
    "date = \"06May\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "edf385ed-ce5e-49d2-af72-586d0b62e4f7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Visualizing our Toledo voters.  vtd, then muni plans\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Visualizing our Toledo voters.  vtd, then muni plans\")\n",
    "plotPoly(countyGeom[mC])\n",
    "for c in pairedCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "for t in areaVoters[0]:\n",
    "    plotCenter(\"v\",tractCP[t])\n",
    "plt.show()\n",
    "plotPoly(countyGeom[mC])\n",
    "for c in pairedCountyList:\n",
    "    plotPoly(countyGeom[c])\n",
    "for t in areaVoters[1]:\n",
    "    plotCenter(\"m\",tractCP[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "fefc4323-d2cb-4280-a654-fc9487b1ee66",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the more popular plan-starters near Toledo\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here are the more popular plan-starters near\",fName)\n",
    "for p,t in enumerate(Toledo5Starters):\n",
    "    if dWeight[p*5] > 0.01:\n",
    "        plotCenter(r3(dWeight[p*5]),tractGeom[t])  #5 b/c 5 districts per plan\n",
    "    else:\n",
    "        plotCenter(\"x\",tractGeom[t])\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "698cb530-9ab1-4bca-ab61-e5be1869529a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "countyTractList[25] = list()\n",
    "for t in range(nTracts):\n",
    "    if countyNo[t] == 25:\n",
    "        countyTractList[25].append(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1c9c6c32-9ea0-4c44-b110-78fd79f8c80a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now tracts in current region NW vote on their fav plan\n",
      "working on tract 2975 in region NW\n",
      "working on tract 3025 in region NW\n",
      "working on tract 3075 in region NW\n",
      "working on tract 3125 in region NW\n",
      "working on tract 3175 in region NW\n",
      "working on tract 3225 in region NW\n",
      "working on tract 213 in region NW\n",
      "working on tract 629 in region NW\n",
      "working on tract 933 in region NW\n"
     ]
    }
   ],
   "source": [
    "print(\"Now tracts in current region\",regionName[currentRegionNo],\"vote on their fav plan\")\n",
    "votingTractList = list()  #PLEASE REWORK THIS TO STD VOTING METHODS A LA LORAIN ***********************\n",
    "for t in countyTractList[mC]:\n",
    "    votingTractList.append(t)\n",
    "for pC in pairedCountyList :     #only tracts included in at least one plan can vote, all three Lucas pC's can vote\n",
    "    for t in countyTractList[pC]:\n",
    "        foundInAPlan = \"no\"        \n",
    "        for planNo in range( len(planTLnos) ):\n",
    "            if planRegionNo[planNo] == currentRegionNo:\n",
    "                    for dTLno in planTLnos[planNo]:\n",
    "                        if t in districtTractList[dTLno]:\n",
    "                            foundInAPlan = \"yes\"\n",
    "        if foundInAPlan == \"yes\":\n",
    "            votingTractList.append(t)\n",
    "            \n",
    "counter = 0\n",
    "for t in votingTractList:\n",
    "    counter +=1\n",
    "    if counter%50 == 0:\n",
    "        print(\"working on tract\",t,\"in region\", regionName[currentRegionNo] )\n",
    "    minDistance = MAPdiagonal  #default set to a very big number\n",
    "    if countyNo[t] in pairedCountyList and countyNo[t] != ppC:  #non-ppC pairedCounty tracts will often choose a homier district ...\n",
    "        pCno = pairedCountyList.index(countyNo[t])        #below few lines define where that alt HD's center is, pairing it with other counties\n",
    "        altTractList = []\n",
    "        for tt in countyTractList[countyNo[t]]:    #... by creating a list of all tracts in the pC and its alternate pairing choices outside the region\n",
    "            altTractList.append(tt)\n",
    "        for cc in altPairingList[pCno]:\n",
    "            for tt in countyTractList[cc]:\n",
    "                altTractList.append(tt)\n",
    "        altDistrictCP = get_PCP(altTractList, tractCP, tractPop3)  #... and this is the approx center of that alt HD for paired-county tracts\n",
    "        minDistance = altDistrictCP.distance(tractCP[t])     # ... so a regional district's PCP has to be closer than this alternative distance\n",
    " \n",
    "    bestDistrict = -999\n",
    "    for planNo in range(len(planTLnos)):\n",
    "        if planRegionNo[planNo] == currentRegionNo : #only consider districts associated with regional plans for current region\n",
    "            for dTLno in planTLnos[planNo] :\n",
    "                if t in districtTractList[dTLno]:   #.. and this tract can only vote if it's in a district (some pC tracts not drawn in)\n",
    "                    currentDistance = districtPCP[dTLno].distance(tractCP[t])\n",
    "                    if currentDistance < minDistance:\n",
    "                        bestDistrict = dTLno\n",
    "                        bestPlan = planNo\n",
    "                        minDistance = currentDistance\n",
    "    if bestDistrict != -999:   #we found a decent district  (closer than the alt HD for non-ppC paired-county tracts\n",
    "        planChoosers[bestPlan].append(t)  #HDtype[t] = \"inRegion\"+str(currentRegionNo)\n",
    "        chosenPlan[t] = bestPlan\n",
    "print(\"all tracts in or near county\",mC,\"have voted\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "698b7fea-e76b-4a88-a566-023bc3a62cbf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "And now the Lorain - Erie - Huron cluster.  Each Lorain straddler fills with Erie or Huron partial\n",
      "We ID'd 74424.0 pop of county 46 vtds to be straddlers, vs 74591 target.  See X's below\n"
     ]
    },
    {
     "data": {
      "image/png": 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tIbfM7hcrALIC85btZFzfxJAsLW2NLT7hlBEqPuxOBwCS6A4pr43GqYmWYUdDQ6PdKS+p4ocvtnPk1xJw1I5AFACzf+pX9C4MNhlbgpG41Ej0BpHkQWmU6gxkJNkanfBDqWOz8fIriN69O2D7v4K0rQYOArIgoHjFCoLAjYLMVWaFHy4tDnqMI4VVfrHiQ1IUMgurO4RgaXfKsqH4EMRlQHRaSLu43KqjsyxqWW47M5pg0dDQOOkU5pWy59ej5Bwqovi4A6nC5LVIqGJFQUaMcGGKEkjqYSO9byK9BqQRFVvX3P/+xixuXLq1yaWVUOvYGI4fB8Ct16OIIoKigKKofwHB+1is5Zgqetv40AFxlZCRVRH03HsmWBEF6ogWnSDQIyEipPcuFML1jzlZfTdZXmDzm7D8dvypd6e/ACOua7Jft1v1G1JEKazxaJxaaIJFQ0OjzSnKL2XXxkwObs2hqgBEV20rimpBkY12kvtHcPbUAaT3arpMR6hLK6HWsQEQZfUO3frMIjKmTKnzmqIoKIqCLMvIkoRktyPb7UguF7IkoUgysuRh2303kbK3gIhaWVxrkxJtYeHMwcxbthNJUdAJAo/PHNRqwqIt/WNa0neT5QXKsmvECqh/l98BGecHWFrqF0h0eTQLy28BTbBoaGi0Ko5KFzvX5ZB/pIy8Y0VUVzgQPT6BYva7oipmB9YEgfhkKwP7ptB9SDqGmNATUoa6tBJKHRsfglew6ILkeBIEAUEQVB8JvR5MJoiJCWhnT/AmLjM0HNZ8+chujOubSGZhNT0SIlpNrLSlf0xL+g4p0qr4UI1Y8aFIUHy4jmAJViDR7VJFk6IJlk6NJlg0NDRahKPazd71uRzZXkjh8Upc1Z46r4u+ZR6Tg6gUPb2HpTBwZA9i4m01hfoO5pH/VV6DhfqCEerSSih1bPzbJXVJQd+SpJTePgRd45fXlGhLqy/XtKV/TEv6DinSKi5DXQaqLVoEHcT18j9tqECiQ3wRMGmCpZOjCRYNDY2wkGWZ43tK2LM+l+x9Jdgr3QFtBAEiok2ccB3EbSxj0NBBXHTltDptwi3UVx/f0spDH21l4abXSKoowgjkrnyEHEEAUVSdYUWRqYqCR5ZQvA6yOoORvT+uRxEFQFDXNxCwudSkby0TLOqkKehP/uW1Lf1jWtJ3SOUFotNUn5Xld6iWFUEH05+vY11puEBiMZACYv1CShqdCU2waGhoNEl+Zjl71udwfG8J5SfsKEHmBYsAcXqB/uO70vcPGYiiyIL536MIMkNH9g9o35xCffW5fGQ3BhbsQf/poWacVXBkUcSa2oLsrl4Li9iEhaUtaEv/mJb0HXJ5gRHXqT4rxYdVy0o935WGCiQKeJ2xNQtLp0YTLBoav1F++eUX7rnnHgByc3OZNm0azz33HFlZWfTp04eP/v01VYet7N69i7dXP4ssS1w4cjb9u6q1xIwWPUldI0nJqSBZR03+i1/zkCd2Q7bqUQQZFIUIqwnFO5HjjaoRo3XIihtFrmViERSUCBlXRYW6hON1dPXtp3h9TBRZVrtBwVxaiAewR0QQ+bcHQadTX5ck9ZiyDF6nWEVWnyuSBL7nvuPIMsgyMUOGYEsObVkqGD4/GKGVSpWES1v5x7S075DLC0SnNRjO3FCBxPXbvOPQLCydGk2waGh0IoKJkE2bNiGKIi6Xi9dee41BgwaxcuVK5s2bh8Fg4G8PzOeV518nVTeEj576lVfefZweCQPZ8OlhUuN68umG17lm/N3ERsfzj6/u5/o/X8Jp49KwRBpxHCqlcEnd+mI+K4lbMRBbXMy5q9dQ+P4HFIZ4DpmfNe/cDU4nGdOno2uHpZg6tOOSkI+28I9pjb7DKS/QYB9BCiR6fv0S0KumG41OiyZYNDQ6KB6XxFsP/oSjyk2XHlGMvbwPSd0ar0R75pln+jNJz5kzhxkzZvDUU09hMBhYs2YNzyx6hrtmP8otN/yVuZc/S2Wxiz/PvpNqZwXnd5/Drpx9SG6Z2MgkAKwxJjyGSu588RKiEyP4fvo/6XGmDUukOunoEyze0sa1BiGo2/VWPb0PZGLy+oW0NVXpXRE6QJZTn4WlPZaEfivUL5AoebxLQZqFpVOj/aI0NDooTruH6nJ1ss89VMaHj/9K37O6cP61A/DIMh6HTERU8LtVj8fDhg0bWLz4Ffb8mM+RrQV8t2o9FSciWPPufqornDiKBfSYqHKW0zNpAJHRZj7d/DHXX34Tn65+lQsvNHHG5AxeWWkiOlF1rIyOjqa4uJiEBDV1uz7aROzMPjXOswLEzuzj90HpUqVOIJ6r/0z6rBlq+npveLDvMQjePzXb/MJDENSEcgJ1t/n+e7cJgoBoNHaMtOwdwMLyW8Mj+QRL+45Do23RflEaGh2EY3uK2fjFEQD6nZ1M7xFJAW32/5zP/p/z/c9FnUBsipXzZw3A45I5vreYvENlrP1xFfFCb/51xw9U2EtZ8s1DFFcWcNOURwAwGAzIERV0H5BE0b9zeOqFRxk9OoWfy5OYOaCK5c+s5sTh7Rx8YiGKpyYKqLS0NCAc2DoyGVPfWNVZNsGCQ1Y4vq+EmCQL+ooiAGLPGEFMenqrv2cdEUHyWVgar9as0Yp4DStC8PJNGp0ETbBoaHQA/vfsZnL2l/qf5x4sY/V/9wFgsRlwVHlqOafWIEsKRccr+eDvG+tsX7/tO87ocz4ANksMf5v1KuViFh+vWcLjH37HmX/8N/feey8xmTEYDAYuuugivv/+e3Zu2cL0//6X/U4nWS4Xb6R3Iyo3j93r19N10CC/daV+XR59tAl9tIndP+aw+r97URR18jjHZQfAnBQfMPaTRUVRISW5OcSmnKTqw74lIX37OVQ0mQK/k+F3zNYES6dGEywaGu2Mo9JVR6zUx14RmOekNoIo+MWMqBMwRYocL93Hov97gdS+0aT1jkVv1LNvXwJr9sUiiiIjR45k5cqVfPTRR2zfvp3o6GhmzpzJlNQ0smbNYl5uDrPj4jEIAnckJDDnT39CiYjgkUceabAuT2WJwy9WQA2+EWVvXpPI9inqt2Plt6x47SX/WCfd+BcGnze5TY8pSr7Pon0sLE2mwNfQOEXRBIuGRjtRUVHBWcPHcujofub+YTGpcT3ZdHAVq3Z8jFFv4toJ9zJ68lDKi+zkHi7lkf/O5pzTfs+5g2awfu8XbM1aTVyqlf0H9vHyy/9g5swZiKLIihUruKBiMqNm9CY7O5vzJ52PKIqIosjLL78MwBNPPME333yD1WplxYoV/jEZe3QHUeTxlJo8JL0tFtZ88w2G5GTKysp4/vnng9blqSiQ6+RnEWQJ0Zu11GBtvcJ+oVJRVOgXK76xrliymB5DR7SppcXvdGs4+YIlpBT4GhqnKJpg0dBoJywWCzdN+TvvfrcYAEnysHLHR/z1oucpUY5y0PUtD153BQBvv/02A3b0oeegBK6Zfza3JJ7n72fo0KFMmzbV73A6adIkJk2aBEBaWhpr1qwJOPZ9993HfffdF7DdkJxMyiMLyH1ovrq0IYqkPLIAgzcvSWN1eRKTUhCEmsLFolxjGdLbTr6FpSQ3J3CsskxpXk7bLg15rV1NpeZvjPrF/UIlpBT47UReVR5Z5Vl0i+pGsrX5eW6CofidWLQooc6MJlg0NNoNEYOiFspL6mHj7BkJDM8Zyl9enYQoiowa9QYAkiTx4Ycfcs11V1FSWEZFsROdXiQy1syWLVvo168fERGtZ8GIueQSrGPH4jqahbF7N79Ygcbr8kRGmxl/TX9Wv70XRQZRqREsBuvJFyyxKamBYxVFYpJbkMU2BESv061O3zwLS7Difqmpl4W0b0gp8NuBZQeWseCnBciKjCiIzB81n5l9ZrZa/8EyL2t0PjTBoqHRDmTuKOT7/+zx13kbc0kfXB473fsk43LlU23PxO12AKp15dJLLyVzdwHb1h7BfHgLggDjr+nPh198yKWXXtrq4zMkJ9cRKj6io6OZPn16gA+Lr+rxwDGpdBsYR1mBHaG0gLIfQRb07eKAaotPYNKNf2HFksUosowgiky64dY2d7wVvJ9pc8KaGyruFxd3TkiWlpBT4J9E8qry/GIFQFZkFvy0gNGpo1vN0uLz4RK0sOZOjSZYNDROAsW5VSx7ehPOepWMAeJSrJgjDFh1ZvLytvHj+nGATHV1NseOvcf777/PO0s/4NbXF/jDNxUFVr+9l8/XfcEDDzzQ6uNtzHw/YsQIMjIy6kQJ1SYy1kxkrJkTv+QAILdjeO/g8ybTY+gISvNyiEk+OVFCotL8GNuGi/sdDXlpKOQU+CeJrPIsv1jxISsyxyqOtZpg8SWOE7RMt50aTY9qaLQhZSeq+ezFLby74OcAsZJbdZA3f/0bm/eu58ILL+Sll55h2/afyM52MmXyYRIT9axffz95ecf5/R8u4r21z/P15v+SdWI/AMcKDtItrQdWqzXguBUVFZx11llERkayc+dOAN5//31GjRrFeeedx7FjxwCYNWsWI0eOZPz48Tz99NMA3PrErWQMy2D8OeMZPns4yw4sC+g/Ojqanj17BoiV2ngq1ZBmWde+E6YtPoH004acnJDmFlJT3K82IhZL97D6STUbGRPbMRxtu0V1Q6xn+hAFkXRb6+XlqREsWlxzZ0azsGhoNJOKigomTpzIrl272LBhA4MGDeL999/n+eeex1mhMGvy/ejsNj744UWOFx1CViQuPe8GJpw7kWGTupHS6zze7PZ3ysrKkGUZo9HJxTOjueP2HCwWkQsutJGQKPLdd6+y7MM97N99hOSYdLol9gVgy5E1XHVL8OUgi8XC559/zt133w2A2+3m2WefZd26dWzcuJFHH32U1157DYA33niDQYMGAapl5XvD9/R8oCcARx4/wt+++VuzzPeOAjVpnGSOrLNdlmWqMvNQBIHI7l06RnbaVkTxWlbqO/yGQkPF/cJxvO1oJFuTmT9qfoAPS2s63sr+ZH2aYOnMaIJFQ6OZ1BcFOYeL+ds9f+fWqYvIzN/H+18v4apz72Ti8Ms4Z+oITpucyAUX/I6n3vkrAJmZmQwbNgyDwcC+fft4/PEnWPa/dYwbZ8Vul4mL0wMiRmNXln/5BH++8Va2/5AJqGv1z774NAPHBHcg1ev1JCYm+p8fOHCA0047DaPRyJgxY5g7d67ajyBwww03EBkZyaJFi3AmOdEn1FwWBJ2ALDTPfO/yChYlQq1/dOLnXRz9+yIMmTsxuisBcFjiUU4/l/QbryLxzNPC6r+zEqy436nOzD4zGZ06mmMVx0i3pbd6lJDkUdChLQl1djTBoqHRTERRxFWiJz+znOUvbqWq7BfiLV3RCQYykgexfNMSzp6RwfDJ4xFFkerq6jrWhCeffJK77rqLBx98kHPPPZeIiDS++boPl152gDeXFgECA/o/xscfr+TSSy/F7XZjiRWY8fvhRCdZiIw1hzzW0tJSoqJqCidKkgTAokWLiI+PZ+/evcyaNYtPvv8EURCRFZnyLeUYuxgx2ozNMt+7C4vQAUpkNMXb9pP3x2uxetRlIsWbktRsL4IfllH4wzKyJ17FkBfmIep0KIqCp7IandXS6SwwPipLHJQW2IkJ8lnWL+7XGUi2Jre6UPEhS6pg0SwsnRtNsGhohIksy6x9dz+7f8xFkRVKcquoTHLicFUTYbEycEwKZ/0+g6WbLZw+tcb34IEHHuC2224D4PDhwwiCQPfu3Tl+/DiPPvoohw4dwmrN4OKZb/LF53MYPmwuXbqM4/33L+Kzzz7jrbfewhRhIK1fbNhjjo2Npby83P9cp1NvRePj1Qyo/fv3ByDRnMj8UfO57/37KPq2iJ539my2+d5TXIIOsB7YQNbs/8PksVMV34vUh+aROGY4iixz7P2vKfn4f1gzN2P67h22jVqNbLZiLD6O3mPHbYjAldKXtPvnkjLh9LDH0B4IvqzDYsO3+/VLGIy/pn+D1jKNppG92YV1+s4pbjVUNMGioREi5UV2fvjgAEd3FvkvkKCmxo9NjqDvGRmUrN7ChGsHADWiAFQ/EZfLxVVXXcUvv/zCjBkzSE9P57zzziM7O5tx48axZMkSXnvtNXbs2EFmZibff38FycnJHD58iH79emI2W5FlgVGjRnH66eFN3r1792b37t24XC42btzIkCFD1HMqLycqKoqCggKcTic6nY5RkaOIWh7F8+88z4g+I5p9VyxX1AgkU3URks5Ij5eeJWFEP//2jBsugRsuYfcTryO/tRhzeQ7U7IbBXY0hayvZjz9NyoT3mjWOk01kuZp/JjoluKNssBIGq9/eS7eBcWFZzTRqUPzlEDQLS2dGEywaGg2wf2M+mdsL0RtEcg6WUlZgr/N6UncbF9wylI23vcnUGwfTr18/Hn/lvgBRsGrVKj7++GM++eQTAM4880wGDRqEKIocOHAAURRZunQp27dvZ/z48bz44ossWrSIkpISbr/9HBReBWS++bqY6OgLQhYr06ZNY+vWrezbt4+bbrqJO+64g3PPPRez2cybb74JwDXXXENxcTGSJPHMM88A8PDDD1NSWMJTdzwFwKuvvkq/fv0aPE5DJM6Yxokje5ET0zEOHEjXS6YSN7RP0LYD75tDxZUXkvfVOmSPROTAvkT3787RV99BfP8VdMU5YR+/PagsK8TqUCfPLj0HBm1TWmAPSHSmyFBWYNcESzPxRU0LWrnmTo2gNMeVvQNSXl5OdHQ0ZWVlddbqNTTCRZZl/nPvj0GLDur0Aj2HJjLm0t5Expj9oqB79+7cdNNNmM1mXnjhBb8oSE9Pp1+/fkRGRmKz2bBYLHz11VcAeDweBg4cyODBg1m0aBHPPPMMlZWVzJ07l0GDBnHddZfz66+fYrGI3HRzHBkZJkBkzOi1nc6/oSGqj+dxdOIEFATSPvua6L7d2ntIjXJw2xrcl99MtQlO37YnaJvKEgdvzltft+6SCNc9NloTLM1k0dNvYzmUgu7sIm6e1fqJFDXallDnb83CoqFRiyPbTrBlRZZfrGQW7OWzX15DZ9BR7SllxsXTufmG58nKyiK+Sx82bdrEoEGDuP3223njjTeorq5m7ty5XH755f4+9+3b5w+B/vXXX9m5cyeDBg1i2rRpHDt2jNtuu40nn3yS++67jyuuuIIpU6YwatQonnhyDkeP/sqOHXb+ekcOvfuYsEWKfPzxwd+MYInomkxVl35Y8/dx4Oa/MuTTNzHaAvPOdBScOTmIgLuRaJXI2LolDAQRxl/d/6SKlcYcfhvDnZeHK/Moxh7dg2ZCbi/8ufpEzcLSmdEEi8ZvHtkjs3NdDpu+yqS63OXfbrEZeHLxzTyt/zMAc+bM4Q9/+AOgRviMGTPG33bRokUYDAYqKysZO3ZsHcECgSHQAImJidx+++2cOHECgB49etC9e3fmz5/Pv/71L7qmDeXoUZF9e53YonQ8/XQKS/9Tytq1B7n00nPa7P1oLZpbwK8+afMfpPAvN2DN2c2OaVfQ+/XFRPcLL5HaySImpQflgK5+stp61C5hEG7EV0tprsNv6UcfBRTFjLnkkpMw4hDwFdxEEyydGc2lWuM3za9fZfLPv6xm3fv7/WLFbDXQZ2QXrls4BtEbdeDxeNiwYQPnnHMOR44cQRAEunWrWZ4wGNT081VVVQwcGOi7UD8visfjYdu2bfTt25fs7Gx27drF1KlT+eGHH5g3734qS0soynMwoP9jxMXpcbsVdDodBsNI0tK8/iRl2XBkrfq3g5GT8wE/rh/Hli3X8OP6ceTkfNDsvlLOO4Oox57HbYgg4sRBsi6ewZ4n/92Ko21FjOo9YChFgyNjzaT1iyUy1kxeVR6/5P5CXlVemw6vIYffyhJHo/u58/JqxAqALJP70HzceW073lDxeTZoFpbOjWZh0fjNUXaimk+e3UJlibPO9qgEM2Mv7UPPoYkB+6xcuZJzzz0XURT9yzcPP/xwnTZXX3013333HU888USTY1i1ahXjxo1DFEWGDRvG66+/DsDvp0ymi6OMDft2M3nMKExxCWBIJsqm57a/6EhO9nDWWWfB5jdh+e341xSmvwAjrmv+m9KKtLSAXzC6zziXwq7/JfOOu7EWHoI3nmYPMODe/2utYbcKsvcWsCkLS23aupJxbZrr8OvKPFojVnzIMq6jWa27NFSWDcWHIC4DotNC309WhYomWDo3mmDR+E0gyzL7f8ln89dHKcmrrvOaOdLA5Q+MbPSC/eGHH3L11Vdz6NAhQF2+qc/bb79NSUkJZ511Ftdff32jCc8mTZrEpEmT+M9//uPfVlFUyLmxJooqVR+N/xt7BoIociyxB8PPOIPrrruOZ599liUvPs3N5U/WhEYoMiy/AzLOD/kiX1FUSEluDrEprV8QsDUK+AUj4YwBxK78H9tueRjLumU4Pv4vdDDBInnUelFyiLbrk1HJuDYxSRYEgQCH3+gkS6P7GXt0B1GsK1pEEWP3VnSCboEIV9AsLL8FtCUhjU5NcW4VX/5jO6/dtobv/7MnQKwAnH/9gEbFisfj4aeffmLcuHFs27bNv3yzYsUKbr75ZtxuN06naq2JiIjAZrM1KztrSW5OQP0ZRZZxVFUQFxcHQExMDKW5R2rEir+hBMWHQzrOjpXfsuSW2Xz46DyW3DKbHSu/DXusjdFaBfyCoTMa6HnXTQCYKvKRXIGRXO2Jx60urXhCzAfSWCXjtsDn8OurRRiqw68hOZmURxaoogX8PiytZl0py64RK1AjwkNd7vRaWEQtrLlTo1lYNDodHo/M5q8y2f1jDlWlrjqv2eLMDDmvK0PO64osqw63RnPjP4PayzczZ85k5kzVXD9r1izmzp2LwWDg4osvpqioCLfbzd/+9reg/dTPi7Jv3z4+++wzJEni0KFDPPLgA2w9lsMPBzIprKji1dUbuHHCKG67/Q5uueOvLFq0CJ1Ox3uvPQf//aCeaBEhrleT701FUSErXnvJL4wURWHFksX0GDqi1SwtbV3AL6p3GjmiHlH2UH7gGLGnNX3eJwvZK1wlfWgTp6+ScW3REm4l43Ajd5rr8BtzySVYx47FdTQLY/durbsUVHyoYREeitVQixL6TaAJFo1Ow/F9xfz86WHyj5TXMXnrjSI9hyRw1u97EZ0Y4d8uikAjqbzrV2MGmD17Np9//jnz58/3L+fs3r2bnJwcduzYgSRJ9O7dm/z8fM477zyOHTuGyWRiw4YNvP/++0ycOJEdO3bwyCOPcOedd/LQQw/x/PPPs23bNm6+7XYK9Fb+MnGsmsjtm7UUJ3VjwJChvPnmm1x//fV4PB7e/GQFd01/AT67Df+VGgUOfd+kCb0hK05pXk6rLg21SQE/r3+DLi4DZ2QSlvIcDt79MGJMLIq9GuvYsfS55Qr0ZlNY3bZWNBOA5PIKlhAtLC2tZNzcyJ3IWHOzIpMMycltE84cl6Gae2qLFkEXkgiHWmHNmoWlU6MJFo1TGke1m58/Pcz+jfm4qj11XkvoGsmI33Wnz+ldmtV3sFDkxx9/nHPPPZfKykr/tnnz5vHvf/8bk8nE2WefDcCbb75JdXU1hYWFzJkzhz/96U98+eWXfP7551x88cVIkoQkSTz77LOsW7eOzz77jLvuuouRI0cya/4j/PuVf3DakDJS+6pp/hcuXMi8efM4//zzueiii7hi2kOk1XFGUELyY4lNSUUQhDqiRRBFYpLDr2PjKXPiKbSjT7Cgjw4UCa1awK+ef4N+2LWwNofIwxtr2uz5ga0rV3LGF0tD7jYn54MAS1Bq6mXNHqbkdqEjdMECza9k3FDkjnXs2A6VIyUkotNUn5Xld6iWFUEH058P3fHWF9asWVg6NZpg0Tjl8Hhkdv+Qw45VxynNV31SMgv28smG1xB1AnapjBkzp3PLgy+QlZWFyWTyJ3i79dZb2bp1Kx6PhwULFjBlypQGj1M/FBkgJSVwAs7NzWXAAFVYmEwmSkpKiIqKIiEhAaPRiM1mY/Pmzej1euLi4sjMzGTOnDkUFBRw2mmnYTQa+f7774mMjMR9/Dg5f7iEr44fY4LNRvWWLYBaLHHYsGEADBkyhJ9WfsklzTCh2+ITOPPam/j5rddAkRFEkUk33Bq2daVqYx4lyw6oE4UAsTP7YB3ZRpNkEP+Gwan/5cidL1H67VowRyCIYP7la6yHfqFk1+GQlolaEs1UVlZGcXExcXFxREdH+7dLLqcqWEJcEvLRnErGJy1y52Qx4jpVcBcfVi0r4UQJaUtCvwk0waJxSpCfWc7uH7I5tqeEiqK6OSMEAUaPPZs7F11NSq9o5syZw8WXqn4m9RO83XnnnfTq1YuSkhKmTJnSqGAJldrWCqPRSFlZGV27diUzM5NBgwbRpUsXLBY1CuPtt9+mR48eiKKI3W4nKirKX7lZBzh27+ZzayRTbDY8ikL5F1/izstjwIABrFy5kj/84Q+sXr2aHpf/vlkm9Pc3ZnH/DwKWrlcT6ynjTxedxeDzhoZ1vp4yZ41YAVCgZNkBTH1jg1paWkwQ/wYBiV6TuiHPeomDT/4T5weqVUUW9IiG0C5rzY1m2rx5M8uXL0dRFARBYPr06YwYMULd26X6TMm6to9nOCmROyeb6LTwhIoPn4VFWxLq1GiCRaPDcHRnIV+8soOUjCiSe8VQXmjnxLEKKoocdaoj+4hOtHDauFQGT0hHXy/B22uvvRY0wVuvXuqEbjKZmhXJE4za/bhcLqKjo1m6dCkDBgxg7dq1PPvssyxevBhJknj//ffp2bMnoEYU5eTk8MQTT3D//fez/vvvUWSFryrKeTmtK5+Vl4Gi4Dqaxf3338/NN9/MkiVLSE9PJ7nnABgTngk9t8zO/ct2ICtQpY+kSh/JQyuyOe/0vhzcU8yhA8XIsoKigMMl8c2efK67dACXjK4b3eMptNeIFR+Kur1NBEsD/g1yTA+2TLmciNy96IEqWxoxf7075HpDNdFMdZ2XG4tmKisr84sVUMXq8uXLycjIIDo6Gtnvw9L2gsUXuVPfh+WUtK60FEXLw/JbQBMsGh2GzxdvByDnQBk5B8oCXtcbReK7RtLn9CROO7erX6TUJpQEbwAPPPAAt912W6uMOzk5mQMHDtClSxecTiexsbHExMSQn5+Py+XixIkTJCYmkpeXR35+Plu3bmX9+vXo9Xr0ej1dunThiiuuIL+0lKP2aiJEkT8dP06+x4OEwvTSEs5KPJOPP/4YSZK4/PLLGT9+PERGhmVCP1JYhVxPaEiKwpYDRRz8zz509dKaT8HI/rcO8GukmTOG1PgB6RMsIFBXtAje7W1BEP+G3ZU3IYyaTEStZoNWfo7ZFrojaXOimYqLiwOdlhWF4uJiVbC4vRaWMJeEmkubRu6cSmgWlt8EmmA5RagfsTJo0KA6ESu33norQFAfjcrKSq6++mqKioq48MILue+++9r5bOpSlFPJ3vW5AdstNiMxXSykZEQzcGxqnQifhgglwdsbb7yBy+XiqquuarK/pkKRn3vuOR5//HFmz57Nrl27EEWRG264gUsuuQS9Xk9CQgI6nY5Vq1aRlpZGUlISOTlqpM7pp5/OhRdeyAsvvEBERAQf/+9/PHnnnTxRXgGyzP/Ky9H/bipnTZnC119/zZNPPokoitxzzz1ERkaqAwzDhN4zwYooUEe06AQBvUtBh4AHhao4AwhQ5ZKIqJKIkUXW/mMnRZfZmTJBfS/10SZiZ/YJ8GFpE+uKj3r+DdVXzaN+CcS9DyxiwKN3YIqODLnbcKOZ4uLiAp2WBcGfJ8deXU4koOgbqX7YyrRZ5M6phE+wnATLlkb7ISj1bxdOUUItT32q4vF4KCkp4e6772bu3LkMGjSI3NxcvvnmGyorK/2C5fDhw3V8NH755Reee+45bDYbc+bMYerUqbz++ut07dq1Xc5DlmWy95VyaEsBuQfLKCuoRvLU/QqKOoE5z56DwRSenvZ4PAwbNozt27fzySef8Nxzz2G1WtmxYwc9e/Zk1apV/PDDDzzzzDN88skn6PUdU6+78/IavGPOcbg4bHfSy2Ii1Wxssq+qajfPP/ADFoeMhGo5lxQFWUB93s+G4XAV0U6o1MO9i8/z75tfUM2/nvyF6CoZDwoJ56Vw9WU1dZKaihIKh9wyO0cKq+iZYCUlumlLjb2ghL0PPoOUl4N4IhtLSRYAbmMkUQ8+To/LJrVoPI3RmA/LNw/9kW4frOfg2O5Mf/3rNhvDSae5KfNPEgsfeJeooi4kz5C4eGrbffYabUOo83fHvGJrBBBqxEowH42ffvqJRYsWAWpK+A0bNnDJSaiyKrklju0tIWtXMfmZZZSfsOOo8gRtq9OLxHSx0GNIAiMv6ImukfwoDRFKgrebb76ZyMhIJk6ciMVi4auvvkKSJK677jqys7Pp0aMHS5Ys4dVXX+XNN99EEAQefPBBpk+fHlaEUUto6I75nZwi5u475l28gEX90rkqNb7RvvYcKCbG7jWDgPdOVKhZztlT5W/riahrFeiSFMHtj47mpcd/xlbopnRlHosL7fz55uGIoog+2hQgVMIVVOB1BPb61ogCLJw5mMtHNuyHkleVR5aURbfnbvVH1ux58t84338DU3UhhW++3aaCZcSIEWRkZASNEsLh9WGxttHyWHvQgetW+fAVm9S1kl+aRsdEEyytjCxLFOUdpry4nMLjuXg8ErKkOvXZYiKJSojDZLFijojAaLZQeiIfS6SNiMhoDGYLjqoKqspKcNqriU1KIcIWi95bCTgcavtolJaW+lVrdHQ0xcXFrXKuHrdERZGDimIHlSVOygrsFB6voOyEneoyF26n1OC+BpOO6EQLXQfEMXBMCrHJ9Q384eOrz1Of2vV69u3bF/D6smXL6NWrF2+//TZPPfUUy5Yt45VXXmH79u1UV1czZcoUpk+f3qoRRg2FxTZEjsPlFyugelzcve8Y4+NsDQqDrOxyVn+XiQW11srIm09DlhQE4McVmZgz7XXa33jfmQF9WCOMzH14DC89uxHj4SqE7WU8/fgG7rjnTEzGupeP5giq2o7AoC5XzVu2k3F9E4NaWhoqFBiR0R2HqAouoayw0WO2BtHR0UE/N1lSBbkYhvUu3O9CcwjXglUzuAZS5odRt6q5hJXBV3O6/U2gCZYQcTldZB/cx9bV26gqEUFWcNkNSG49gigh6hUEQcJZYaOqEl764m5yizOZ+4fFpMb15K1VT7ErawO/O/06zh00A4APfniR40WHkGWJaWdcz8D0kfy092vW7/0CRVEY0mMMk4dfiah3YIiowmxzcmTHIZb/YzmbE7ZhsMhs3PojHlx8F/c+ligz1qgIvl69ltKiE8yYPg1ZlomJiaG8vFytQ1NaGuDXIUkeqsqKqSovpbKshKrScorzyyjPt+KuTKDgaAUApgg9kkdG8igo9b03G8Fg0mGLMxHf1Ub6wDgyhic2mQ7/ZFI7x8mIESP45JNP6N27N3a7nYqKCuLj1Uk33AijX375hXvuuQdQc7VMmzaNu+66i6uvvpqjR4/So0cPJkyYwM6dOzlx4gQAP//8M9nZ2X6fCP8Y7c6AAFwJOGJ31hEsFZUuXl60EVueeqfvm5pkYEj/BCxmPVUb84gqcfG5ScalgBhhYODYNBLjgk9ker3IX+85i9f/tQ3HxkIijzt4+qEfuWXe2cRGqRaW5ggqaNgROLOwOmBiDVYo8KlVj5E2bwVRO1ZjBlymaOL/cnuDx2trFG/xQyFEwdLY8lJrEa4Fqw4tTZnfTMLO4OsdYmtF/ml0TDrOrNFB2bxqBZu/KsVZEQuKCDSdEdRgcHHLRQ/w6S9LiEopID7NwPXx09i0vQdVVdXojNVIbhPnDbmExNgEquzVvPz5PAamj6RP6lBGDZyAqHex6MMHGdV/KjZLLM5yM85ycFZGUHkiiQpJXQ4qz4/F6Xawb626XLQ/ewvfb1/BjVMe5Y27NwMgHU9g3uynmTByLG9+/CZ/vGgOb2x5F4/LgOQ0I7nM1C1WpwN8E2aFf6uzOvhyDqiWYp1exBxpJCreTHxaJN0GxtF1QCx6g3rn+8svv/Cne24BaibwTZs2IYoiLpeL1157jUGDBrFu3Trmzp2LKIpMnTqV+fPnh/RZNZcBAwbwzTffcPHFF/Pdd99RWlrK1KlTGThwIJIk1bHQQOgRRmeeeSarV68GYM6cOcyYMYM77riDM844gwkTJgBqhMmgQYO44447KC8v5/rrrw8QKwC9LKaAAFwd0NNSd0nmhw3ZfrHio9QikDE2FYtZ78+hEiWIXOXbV4Dkc5quXTPnj0NZlniAY19mEVsq8cpD67n27tPplhYVsqCqT0OOwD0SAh2sgxUKvH5VGlE7VgNQffpUBj73EJak2CbPpa3wCxZd05fWpkKkW4NwLVgBtDBlPoRf66g5GXwFSb3GWEJchtQ4NdEESxPs/uEEznL1RyIaq4hJLaXHEBuiTk9ElBlzhAXJI+N2OZFlgZj4BFJ79sNgMrNr1ndccMN0Bg0aBKhLE5WVldx864UAuJ12dHoj1XY7H+5cyLWPD8ISORqd3ogoiry//VmumD8Is06kOD+PObf+lYOFh6ncnsnFF13A4cNHWXfwFySPRLV+H1dNuYYPPnwGo97C4i/uwqAz8+dpTzC6/zSWrlzIT7tWMaj7KPRVg6iuCjxXUe9Ab67GYHEhGPLQGSsRnKMpzRUxmER6DUvClmAmMsZEZKwZW7wJW5wFgym0iIhgE/hTTz2FwWBgzZo1PPfcc/zrX//i6aefZunSpfTv35+xY8dy++23ExMT0+LP0mN34iwqR0EG2WslUmDcwOF887/POOfs0Qw9fThJSUm89tprHDhwAJfLxXnnncekSZMQBCGsCCP/cb25YV5++WX++te/snfvXqqqqjjvvPNIT0/3h8V+8sknDfoWpZqNLOqXzt37jiGhipWn+6WT6jwBuTXOkB6pZmIZcFVvRp+VirmW83JLc6jMvKgPaxMtbHxrHzEOeHfhr0y5eRC9eseEJKjqkxJtYeHMwcxbthNJUdAJAo/PHBR0Mg1WKDCtUF12dE66itNfCl508mTiEyyivull3KZCpFuDcCxYQWlhyvzm1DpqTgZf0aN+xyMiOpHvkEYAmmBpAr1R/eFkjM5h8jVXIIqt95YZTOqP629/+xt33PFXouKS/K999tln9O3bjx69+gGQ3L0P63/5tck+r7r/Wv9jh70SZ7Va8+ay4lcpKyqiqrQCUe/AZDFjtkZgjYomIiqGyOg4DIaayWXFN4MRDdX07fYH0nsPbpXz9VE7uZvPhFteXs7gwepxBg4cSFlZGW63G51Oh9kcfpG2+pQfziVzxu8xuCqCvn6r9+8LH/6PGa+8zPr16zGZTOj1epxOJ4qisHr1aj7++GM++eSTsI7tyw1TXFzsD48WRZF3332XG264wR8Wu2zZMj788MMG+7kqNZ7xcTaO2J30tJhI3f1egDOkxXyuv/2enYX07BmD2awjJVH1EQo3h0ow34dxo7oSH2fh88XbiHTD9y/vYOgVvYMLqhDueC8f2Y1xfRPJLKymR0JE8Im0LJvk4kPMH3obC7a96PdhiTHFAODZvQ3J6UJnat87bEVSBVQoPixNhUi3BuFYsBqkmSnzm1vrqDkZfHVewWK1tPxaodFx0QRLExi91069QdeqYsVHsDv27du38/zzz/PFF18ABI1iMRgMZGVl0adPH3+dnD//+c/s3r0bqOULEa9eGKLjk0nvE/q4FMW3RNSw42xzqZ3c7cSJE8yYMYOsrCw+/fRTAGbMmMHFF1+MyWTimmuuaRXBcvzr9Q2KlQKPxN05x9EJAqMirKQ9OI+JoycwatQoJEnilltuQRTFoBFGoeDLDRMTE0Pfvn255pprWL58OaIoIssyF1wwgaxjqxFFmeQmTOapZqMqAhpwhhx3yzb8bsXbS/ly+6/IKPS7ojeTx3cPK4dKY74Pp/WLJ/bBs/jPk78QXQ273j1Il0lpbLxgYI2gMhsp3LyPrFeWYty+Fl15EQBOSxyK3ogiiKrQEkUUUcTkcZMr6vBl5PHPsbIEsmq56A78S0xEEUQEQcBSuhUAa/Yutt/2KMNffTSkz6StELyCBV3TVsfo6GimT58e4MPSmo634ViwGh9s+Cnzm1vrqDkZfHWyatHylcDQ6JxogqUJjBGq17mzqvUn7lWrVgXcsefm5nLTTTfx8ccf+398waJYLr/88oA6Oa+88goAx44da9AXImS8gkWRW/+8fRM4QGJiIj/++CO//PIL8+bN4+uvv+auu+5i3bp1dO/enYsvvpg9e/b4iws2F+lEAXqgutcIhn/2JoiCXzA8MXs2psOHcZ4oYYpLjZw5xxTL01u2+MWg0+lk/PjxHDhwgKioqJCtLB6Ph59++olXX30VURSJiYkhIyODG264gY8++ohrrunHseM3svTNEoYOFcjJ+SC0asENOEMaKzIpSzQQfcLt3ywicCyzJnOwdWQypr6xjeZQCcX3IbWLldseHcOLj20guthD0YocVrgkrr/yNH8/R59+mYgtK+r0bbK3TpRafeT9u9qk33AwKKpQcQmh/W4aDZFuJUKyYLUBLal11PwMvp0irZhGA2iCpQlM3twUTnt4P4RQMqQGu2N/+OGHKSgo8FtcXn311aBRLGeeeWZAnRwfH330USvkWVGFWn0nx5ZSewL3eDyIoogoikRHR2O1qssWvueiKGKz2SgrC0zTHzblpQDIkXGItbKQbt26FafTybp16/jfv//LO3Nv476kLryXf7COGHzppZe44IILuOiii8I6bO3cMACPPfYYF154IW63m7///T6OHX8YkFm3toqH5ieFXC24MWfI+xakUO2QUGSFF//+EzGlEnpD3eiJYDlUahOq70Ok1cjdj4zl+Sd/JuKYnbI1eWwf3oUh/b3Vn+ulSnd37YcztR9xk89FbzYhSxLV+48gvv18zWncPA9TF3V/oeQI/PqG+m30daUAp89Gie2BotQUn+w+6ezG37OTgC8fSGUD1rxgNBQi3ZqkRFtOmlDx0dJaR+Fk8FW8QsXjaTgwQOPURxMsTSCL2UASFaX5Ye335ZdfBmxbuHBhnefBcoK8+uqrAduCRbE0VienKV+IkPAtCbWyYKk9gefm5nLVVVf5RcvLL78MwPz585k2bRp6vZ5+/fpx1llntfi4isNb4dlUd3nJl/FXURTKKsqJ1enIcktYUxPpFlVzgf/666/Jz8/n2Wef5corr+Smm24K6bj1c8OMHj2adevWAVBc8hNbtqjv7/Mv+KLPmq4WDDTqDCkCkRHez89bNNJoDC9VfDi+Dwa9yF33ncVT96/DVi6x/MN9DPmbKjhEmw0A+6iLGPHGkw0e7/jIwVQePErs8IF0GVOrenRZNpx4IFCYXfP7Dplxdbcun3Qg78C29h5Kh+Bk1TpSvErRLWmCpTOjCZYm0BnVEFHZHYaTWitz4YUXsmrVKiZMmMCgQYOoqqoiMjIyaJ2c48ePo9frm/SFCEb+8X2UFWfh8ThBUH/4Siv4sFRm5eGqUMOSTk/txem33EXxrkNYgP+98u+ahhIU7zrEeeMnsGHDhhYfF8BRXE7hhh3IG1YBINZLdpaQkIAoigwYMAB7ZRVvRkfzclEhc8dM4Zmlr1G4cRd5RU4y9x/kyvFTuWP+Ii75602cmdiD1C7qe6wocPtjf+NobjaCIPDCg4/SM70bx/NyOfuSaXz31kcM6N2Xe554hH1HDgKweed2Ni3/FDk7HlDUW/MoOygiziM6inMOIQhe0SEIWNMSMUbVS64XijOkV7C43TIniu2IihO96K7TRAhiRo9E4fEpqTz19T5/Irh7pvTFJpVRWVzm30NRFL8VfsSEGA58WkRktoNDOw+RmhhRIxBPZKPIMkIDeTK6Th0NjA7Yrphikcc8BCsfBUFGEASEC5+EyC4IiuK34KjjUHyDqvnv8aC4HcgOO4rDjuKsRnFUo9ir1L9O73a30+8nIwiC2q/o9bERBG9CslrbUf8KCKqDjyCCACbvfGl2wW1fvIDivfdXrUA1j72P1H++17zb/S0Vn92gpp1S8857T1ep+1nUvOp7UKtFvcf+JnKt12lyv5pj1z1O0GPUfnw4mJW6pkWFw01umXpj0SfJ2lAvKPWPpcAkfgdogqWzowmWJoiOzgBA9rRf9IEoijz33HMAPPzwwyQmJrJixQqmTp3Kjh07OHjwIKtWrcJgMDR7OejI3vUczqmJMNL5UnS0sPrpnmeWwpInwtrncLehnPHtey06LkDRtgMcv+5ajM4y/IsfpSV12nzzzTdYLBb27t3L2k+/4rFrLidGp8O24B5cuTmU79tFiclExIl8Bi99jfL//otBJwrYevsNmL1LWLsdDiqKi3gjNY31VVW8fMPV3JfUhafz8xhuMFBy3x2cMJm423vMXLebeTodwl/uILCik0wJgWnPPXoLPb/8kshu9YSo1xkyt8zOkUOFAZlMfUsUFWvz+WCtaiVMNezkgtjHADAIjvqrNn6uAK6obZBa7f3fAJMUKDM8RYG7D18vPqoeK0emP2A5uIn9Qwaij/T6RkkKiow/tFyRvBrDAyCgj1CQnKBIvsHVVIvmo2eBZxseSDsy3vtX0sGqwtfbcyinHiIYvSl0jrobb1ofRVCzTheXaz4snRlNsDRBZHQa4AK5/Qoq5uXlccUVV6DX65k4cSL33XcfDz30EFC3Tg7Axx9/3KzloMqybABkSY/sSAPFiF7oTteM01s09urtO4kAZEFEERpflhAUBVHxYMw71KJjgloc7+iNf8LiLMNtsOKO7oISFUfKDdcHtI2NVa+Sqaf1YWVVFYMjrMzJzuWAw06m5wRLMvozNDKaHZLIGRYbe125/CExBrfRBIpCnGBEFstw6SMoEZxEGyxkyjoUUU8Xo4hHZ8Sj8838Cl+XljMxKhZJNPq36eS6V2jZF5GmgKh40HvsHPzDpQzbtC5g/I1F81i7RcK+uv4UOe5BLCl4F4Bkwx5mxs1rULSEgyDA2ZFv8WXpPDyKer4lMX1wGSIxuiuRPQKu0voTSvADe6rbIMW6oCCIimoM0fn+C4g6n5WkVr0ln+FIqWdBCLbdvw3KFSMlBpGNPVJIFNRlPlX0C/5/eJ/5LDY1W3yPVAuOIFBrv9ptal7znxoCCDU9B7xa6+0U6rznNa/V2S7U7kftu+5+wfurGUeQV4KMofbrZQ43e3LKMRv0DO8WA4DLVUB11SF/W6u1N2Zzl7rnKQgoinr9i9LXrbem0bnQBEsTmCwWwIXdIbLqwJuc3f0PWIy2kzqG5ORkf8K1+tTPwurzjwgXkyUGqkB2JjFl+spm9REU73XdPeEShr2yoNGm+T9uo/iPVyBIbjx2B/pm5FTYdPmtGHf9iN7jwIKaqr37Bx8Q0y94ZMLkyZN56623OPfcc3E6naz5YR2jR6tLEz4xOGjQIBbl5zNr1iz+XV7O1HvuYtrfapKUybLMkmuu4fLNm3HqFH7cspFHHnmEhV4fo77ePnz8dM45fPjhh3WW7TZdcycRv35FVepp9HxuIfFDa2LQf734Jqy71mKormsdgqajeW7/60iyV3/Hqo9PUObuErB/gbs3jrnH0JnMgfnkfBvqqZmaCU70P/e1SAHmICCKNRsF4Urkqgrc+7fgydqPoNMjGExgMCAYTQgGIxjMCEYj7v3bQadDF5eEGJeELiEFMUYdt+J2gtuJ4nahuF3gcXlNM7K6nCMIIOrU/4IOQdSDTqcew2RGMFpAZ/Qu6bQNT/1jCa8cTWV2Wg5vXndDg+0qigopyc0hNiUVW3xCm43nVMbhyOXH9eOom45wL2NGrw3w8Vr0zfcA6HVaav7OjCZYmsAcaQPK8Lis3LZ+AdYNTzOn3wXMOTO8ZY6OjsUaB4Ug6CpbuWd11gtlaSl+RD/yzDEYHaUcfuMzul0zjcvHTeRYQR4IsGDiTNJj4smtKOX3bz7Hu1feQu+EZBauXs4RxYkiwg0nChhrjQTAZY4h4YlnGxQrADqdjnfeeSfoa7XFYJcuXRrMu1J7WWnz5s3cfvvtxMfHh+VjdPp/n8VR/DDmuEBLXu/H55H7+7UEs0Y0Fs3TxWZizwOPcni7nbKukzDpqumRWoIoSKrPBQLdhqZgsbW99VC02jANH4dp+LhG2xkHjWrwNcHU8ZOCebwhvAZ9w9/3HSu/ZcVrL/lzr0y68S8MPm/yyRpiq9OcCt2hUG3PhICCD4FO6bIsY/I2i4ttOluzxqmLJliawGBSL+YG2QSKQJWs8MKeL+gR04+JfWe38+haB1mSMJrUSV40VCHLcqsVEau5S2+6rd5iRho6Fn7+nPLvv+ejD9/FfXg3b3t9Qz5a/h/uS+rCW/l5jDCZMPywHKPJxP+5XKRabLiu+iOz/n4Pp3fpTb9PPsDcJRad4eR8xX3LSjExMXz66aecddZZYfsYBRMrQKOFJhuK5knTSWy+5Gasu9fRG7D0SWTM/BuwxLbf0ubJpLLEQWmBnZgkC5GxJ0/ouL2lEfRi8OXPiqJCv1gB1VF2xZLF9Bg6okNaWjxlzkbz9TSnQneoRFh6eHutLVpELJbuddqVV7gQvReYxLj2C47QaHs0wdIUQq0fqaAwPiGN1YXZPPbr8yRFdmNI6vntN7YWsOH756iQliDoXAhCzWwniBIuRyXmiPaZ2KwjRyD//DnWXWvp4c2poCgKhT0GE1lRyoFuffDIW0j0uLH3G44r5xDplIHkhKWLEQA5tgvWridvLbv+stLq1asDlpVa6mMEqi/LtqFnISgygiyDIiEoMsuDhJ5X/k+mdpzFqHHWVhUrTU1k7cnuH3NY/d+9+IKIxl/Tn4Fjmi5aGm6RPlArSGeVZ9EtqhvJ1mTc3qgsvT644C/JzQmsHyTLlObldDjBUrUxLyAjsnVkzfvS3ArdDVFWVlYngZ7ZnMKA/o+xZ+8D3t5FBvR/LGA56ESJGlnkQcEaoU1pnRnt020CnbnGNKCT9dx5xiJ2fHcVhW6Zq1fcwYy003h0YssjWk4mVRUlVEhLEPXOgNfclT0wmiNb7Vi+i3Oo0UZpM87j8KuL0HkcmBN6Ilc6uajajnR8Nz/++COPPPIIT734KQ8//DCnzZ1Lr8QU9lx7C7rCbJ7P3M01sbHo+7YsK264hLqsBM3zMbKmJeI2WDG4qzA6y0Pez2OIQO+u9g6y9Uz1TU1k7UllicMvVkC18K1+ey/dBsY1amlpTpG+ZQeWseCnBf66RvNHzcfjy3vTgC9FbEpqYP0gUSQmuWlBdTLxVfWu7WxcsuwApr6xfoEaboXuxkTu5s2bA0oUjBgxgtTUy4iLOwe7/SgWS/eg+Ymqqlxq/wKtZhnW6JhogqUJoiNtKMgIiBg9FuIjunJRt1G8cXg9AJ9k76J9q5eEj6O6DEEMnq9Abz3Kt1+MweFOYYMbrIZhZMQNpn9qH/qnZxARbq2OMJaEACK7dqH/j2tRJJmVG34g7aOP+Ppf/2rQNySiSzynf/seb7zxBpZ167n2/24l4Yz+4Y2xg2OMspLx7TeU7z6MYNAj6vWIRj2CXkQw6BFEnTdPiA9vluJPniX/jW8wRAlEXBAYKt0cQpnI2pPSAjv1DBgoMpQV2BsULM0p0pdXlecXK6BmhF7w0wImSP8HgL6B4oe2+AQm3fgXVixZ7M9LM+mGW0+adSVYMctghFLVu5fFFHKF7sZEbllZmV+sgHqTs3z5cjIyMvyWlsYSKRoN6vKbJlU6P5pgaQKdTodL58AkRWCSLDgcdr9Y6UiEWiBx3bp1zJ07F1mKZOTwDK6+dBgO5xE8ZCEacxANdgzWAjZXFfFFhQkcB6DiQzgKwnqBKHc8SUIqaaaudIvqTkZCTwak9iGja3eM3mWPOngv6P4kaCFQO0FaKL4hP/zwg78mU0MTRWPIsszs2bM5fPgwgiDwxhtvkJGREfDe+dr6Ck3eeuutTfTcelhT4rGmhOcbcCJfLSNo7Z+C0Iz3JRihTGTtSUySBUGgjmgRRIhOanhybk6RvqzyrICyFbIi4/QuYzYWrTL4vMn0GDqC0rwcYpJPXpRQY+Hv9Qmlqneq2RhShe6mRG5xcXHgMpmiUFxc3HDJgrJstZ5WXAZGo7ekh5aCpdOjCZYQcOrtmKQIjB4L1fYqZvca7RctDw67lrWH3sHpsVPmLOJQyX4ckgOTqCMjdiB/GHQXX+x5iRe3LyXGYOS6gVczfcBfWt10GWqBxKeffpqlS5fSv39/xo4dy+NnLiEmJgZQLxKV5bmcyN3LrwdfBvYTKeixOdMo1OXj1jkoMxZSRiEH5O1Qivr/oLpcFutJoouQSpqlKz2ie9A7qReV0gkMadDLGP6SRKi+Ic2touyjdj2hFStWsHjxYp577rmA9w7g3XffDVq/qSOiT0kDtuMpCX0Zqck+Q5jI2pPIWDPjr+nP6rf3osiqWBl/df9Gl4OaU6SvW1Q3REGsI1rU5+ol1dCEQLTFJ5xUn5VQilnWJtSq3lelxjM+zlanQnd9mhK5cXFxgctkgtBw8dbNb9ZUKhdETGMXAynooFUDBjQ6Hi0SLAsXLmTevHncfvvtPP/884A6cb766qts2rSJoqIitmzZ4i/cFwrvvfceV155Jb///e9Drojb1rh0agVfo2TB7qzmL6Ne4H9HR1Iqwd+3vtXwjkc38689H5LtVH1FCtx2Hvz1ddZmfcczv1veqmMMtUDiwIEDKSsrw+12o9PpMJtrLuSCIGCLTsUWnUpK1Woo2k/PyAjeue5zZFkmuyiP3Vn7OVBwmMzSTI7bj5Ev5VCsL0ASPRQacygkh12eX6EI9f80AD135OYxJMxzCtU3JFhNpnCoXU+otLSUxMREjhw5EvDeSZLEBx98QEVFBc899xwffPBBg9YYnyVLFEUmj5/I/XPuCrp235b5OAzdegHgLq1utT5Dncjak4FjUuk2MI6yAjvRIUQJNadIX7I1mfmj5gf4sCw7omYTNukbT5J4sgm1mGVtQqnqDaqlpTEn26ZEbnR0NNOnTw/wYQlqXSnLrhErAIqMsPoJ4IUGE9tpdB6aLVg2btzIa6+9xpAhdaehqqoqxowZw6WXXsoNNzScOCkYR48eZe7cuZxzzjnNHVab4NKrgsXksVDtrMagN3N174m8ffB7RAH0gohBFNALItEGEykRCTg9Dn4uyfGLFYBEg8gJt8y3BZnklu0nJbpvq40x1AKJM2bM4OKLL8ZkMnHNNdfUESy1MevV8ECXrNYSEkWR9MRU0hNTmeJPQK7i8Xg4nHeUPccPcLDwCJllmeQ4jpGv5FJmKATA4s1c2RGpXU/I6XT6nXvrv3dvv/02I0eOZPny5fz1r3+lX79+DVpjfJas9IoYJsyczGX2UURbbHXW7ts6H4e+q1pWwlPR8npQtQl1ImtPImPNYYUzN6dI38w+MxmdOppjFcdIt6WTbE3mGed/AeiT2oB1oJ0Ip5hlbZqq6h0KoYjcESNGkJGRUSdKKCjFhwIKslbK6jm4UTTrSienWYKlsrKSq6++miVLlvD3v/+9zmvXXqvWo8nMzAyrT0mSuPrqq1mwYAHr1q2jtLS0OUNrEwTv4qhBMuF0qeLl5rOf4+Ymqtmv2fdPbt3wsv/5Cbf6Q4sQwWZu3bDbUAsk3nXXXaxbt47u3btz8cUXs2fPHgYMCIyqMenVux+X3HQxMb1eT9+uGfT1TpC1mfrMILITBJLcrXPHGaq/ycsvv8zTTz/NGWecwUcffdRon6EkfpMkiffff5/XX3/dfyfYmDVm4MCBFGWfwPRNLqIgYtIb66zd2z0VJyEfR+tW2q5Na0xkDVLLP4HoNJzVVRRkHqai8ARupwOdwUhkXDxJPXoREdXAxNYMDMnJYVcTTrYmk2yt2cfpXRKyRp7cbNhNkRJtYeHMwcxbthNJUdAJAo/PHNSo420o1A/rbohQRG50dHTDQsVHXIa6zldLtFQqMQB4NK3S6WmWYLnlllu44IILmDhxYoBgaS6PPPIIiYmJ/PGPfwwp9NPpdOKsZb0oL2+9dfr62CxWKAWdbGBI3zNC2mf5rheZ9+uSOtssosIAWxJ3nvkAkabYVh1jqAUSRVEkOjoaURSx2WyUlZUF7c9nYXHXd0YMk1Kv/2xSUo8W9eMjVH+TSy+9lKlTp3LvvfeG1G9Tzr1vv/02+d70/Pv27WPu3LkkJCSwcePGoNaYGTNmcPGMi9E7YebAyZj13gu0d+2+xN72+TgUu1ohW2gk62qHw+uf4JEU9lZ0YYcwitycYpQguWYAEnv04rRx5zH4vMkYLe2fNMypqMLcaOx4WXkvH9mNcX0TySyspkdCRIvFSrCw7pl9ZjbYvlVEbnQaTH8Blt+hlmUQdFQOngNrQBJPoe+5RrMIW7C89957bN68mY0bN7baIH788Uf+9a9/sXXr1pD3WbhwIQsWNF6bprXQWdSL0PiUCUSGcOf0yk938I/93/ufX5DSl/vGvkSkMRa9vm2cE0MtkDh//nymTZuGXq+nX79+nHXWWUH7s+hVpeFqgWBxO+1Ue6/bycNHN7uf2oTqb5KUlER1dWi+G6E493bv3p1ff/2Vr776ioULF3LZZZcxevToBtPw33XXXaz+5ntM7+Zz47K/caAwkz4JPfxr97Gets/HoTjU8xdPFcHi9U84WhnFitzelLktgLqkGJWYRExyKkazBY/LSVlBPiV5OZzIPMzqzMP8/MmHTLhuDgPOmdB6Y6ll5QkVl/eSajR1vGUyUC0tLRUq0HBY9+jU0Y1aWlqFEddBxvlQfBjiemHf6AKOIHUstyGNNiAswXLs2DFuv/12vv322wZ9H8KloqKCa665hiVLlpCQEPqd5f3338+dd97pf15eXk56enqrjKk+okG94LtdofkCrM/9tc7zL3L3s27ZFJ4c/QBje13R6uOD0AskTpw4kYkTJzbZn8lnYWngzjYUSgqzUbwJ42LjQ7/oN0ZT/iayLHP99ddz+PBhnE6nP9Kg/pLR7bffztatW6murmbu3LkhJ34DOPPMM7n11ls5fPhwg6HWoigSn56E6eIYIj+PoMJZVWft3oapzfNxyA6fheUUsZUXH2JTUTKr83sBAla9kxGxOfT/41NEDb8goHl1eRkHfv6RTV98QkluDl8ufobcQ/uZcP2NIScqDEq9KBSmv6BOkrUI5iytSBJOVOdTk+nkR03VzxTbljQU1n2s4ljbCxZQRaRXSFZX7wdA0Z0iwlyj2YQlWDZt2kRBQQGnn366f5skSaxdu5bFixfjdDrR6cKTuYcOHSIzM5Pp06f7t8neu3q9Xs++ffvIyAj0jTCZTJhO0l2MzitYZHdogf6X97uCPZtexVWrebkE/9v/TpsJltbGYlCz3bobqWPTFMVFxwCwOsBgaJ3Pqil/k7179/qXjN566y0eeeQRgDpLRrIsU1xcjCzLGI1GHn74YS6//PIAUbNy5UrmzZuHwWDg0UcfZfz48SGHWte2ZPUZ3ptJ8y/HkBhRxyTe1vk4FLvqb3WqCJZdewtYna/+1gfH5DK+y2GMOgF6DQvaPiIqmqGTpjFowmR+/t8H/PTRO2z5ajmmCCtjLrumeYMIEoXC8jvUO3rvBNmQs7Tb5fB3YwyjUGNr1D1qKFNsW9FQWHe6rW1uGhvD5fTeSGqCpdMTlmA5//zz2bFjR51ts2fPpn///tx7771hixWA/v37B/T54IMPUlFRwQsvvNBmVpNw8AkWyR2atWH6abcyrtdlTPloIlW1JvzrBt/WJuNrC8x6r2BpQTKm4hI1cVlUKznc+mjM32T37t306NEDRVEoLy/HZDIFLBlt3boVt9vNunXr+OCDD/xLZ/X9YObNm8fXX3+NwWBg2rRprFmzJuRQ61AtWW2Zj0NxqktCp4JgqSgu5Pt31RIXZ8YfZ2ziEQRRB9Ofb3JJRqfXM/rSq7DFJ/Dtqy+yYdn7ZJx+FskZfeq0czhyqbZnEmHp0XDm1CBRKCiSuvwQndZo8UJ0NT8Wkzk0C0tz6x7VpqlMsW1BQ2HdzbGuNKeGU23CKbCqcWoTlmCx2Wz+jJ8+rFYr8fHx/u3FxcVkZWWRk5MD1OTISE5OJtn7ZbzuuutIS0tj4cKFmM3mgD59iczqb28v9AYRCZDCmL2jLUm8cv6LPLDuHo477IgofHvwTTLihxFp7lhFzoIRYVR9ddz185yHQXG5mpMiytN6dWyasnDceeedPPHEE3Tt2pWioiIiIyM555xzWLt2rd/a4vODueqqq/jiiy+YOHFig3lXfN9FSZIoLCwMa9myvVEc6h2/aOj4i/ubv/wMt9NBSp9+jL3rHwilmRDXKyz/kcHnTebYru3s+WE1v3zyIRfdNc//Wk7OBwFF9FJTLwvsJEgUCoJOHQuNFy+MiK9xpDcamv7ON7fuUX2alSm2FQgW1u0j1OWp5tRw0vjt0uq3Xp999hnDhw/nggvUNecrrriC4cOH889//tPfJisri9zc3NY+dJuhN6oXfKXpCN86jEgdzxeXbmCILQYZgTczt3DPd1cGXFw6Ima9KlhkBFweRxOtg1NWVQyATWm9pTufhWPNmjVs2LDBL1ZAtXBkZ2djsVjIzs5m/fr1TJgwgQsvvJBevXr52/n8YDZv3kxMTAxbtmzhiSeeYO7cuXWOZTQaycrKIj8/n507d3aoUPtQkL2CRTB0/ITWh37dAMDI6RcjxKZDz3PCEis+Rl50MQCHt2xE8rgB1bJSI1YAZPbsfQCHI8g1yBeFInhFnlDXyuMrXlgbn7O0y6m+30bciI2k5vfRWN2jcPBliq0zpsYyxbYiydZkRiaPrCNWNm/ezPPPP8/SpUt5/vnn2bx5c9B9G6rh5M7La/Nxa5yatPhKVt/Rc9asWcyaNSusfeoTzNGxPTEY9TgBxR2+zVEURR4Z9wIzvrgegHVFeUx+bwRPnfMkw7s2nCTsp8xlvLBpEQ7JjaQoJJgiWfy7/2Ft5XDohrAYovyP7e4yjPrw19adHm/CPYLUGGpDmgpRvvfeezEajezdu5cNGzbwu9/9DkEQ/H4wPsfdqqoqhgwZwtlnn83gwYNxuVyYTCa/j8usWbPYtWsXVquVCy64gLvvvvuknmdTKH7B0rEtLLIkUZKrWmRT+7Ws0nZCtx4ASG43+39ez4Ax51JtzyQwJ42M3X40+NJQvSiU2sKpseKFRUWqADIS2p1Nc+oeBSOsTLFtTDjLU82p4aTx26bj33p1AExmA5U0T7AASLKrzvM8l4frvr+LAVYrr1/wGVGWpDqv78pdw41r5tfZlulw8sORj5jSP7zswc3F7HW6BXC4K4m2dAm7D6ekTpgm4eR9zUJxij127Bjr169n/PjxVFRUUFlZya5du/yiZuvWrfTt25etW7eyYsUKPv74Y0pLS3n55ZcDagu98cYbHWbpsj6KyydYTq5gDJf6od2thewtRBhh6QEBdYVFLJbuDe9cKwqlPg05S7tcal4ooxBaNGFz6h41RMiZYtuYcJanmlPDSeO3jSZYQsAS4V3ScDXvYto36WzuGTSTNcfX0dWWxsfHtgKwp6qK/259jHN7zESvM6AXTZTa87lh1T34PMgmJfVgZ+kxcl0SVe7KVjib0NCJenQoSAjY3RXN6sPhcYIejMLJmzBDcYodMGAAZ5xxBtnZ2RgMBtasWVNH1MyePZt//OMfLFy4kLfffhuPx8Nrr73GBx98UMfHRRAEbrjhBiIjI1m0aBFDhw5t8/MLB8WbWFE0dsyfeZ3Q4IREKgpPkH/4ID2Hnd70zg1Qmpfjf9znLPUzNZtTGND/sQAflgYdb0PA5yyd43CxraSCXhYTTqd6Y2ISQl87DrfuUWOElCm2jQmnkGFDNZykGIWKkp8ad47W+E3SMa9kHQxrhBlwILQg2uXa0xdw7enwzw131dn+j/0r+cf+lfVa11hyFk35lKuWjSbXVYXdE97adjAOF/7C5/v+w09523DJHkRBUP8johPVvyadgUhDjVna4WmeUHJKqmAxia3ndNsaNCVqZFnm1VdfZenSpY3WFlq0aBHx8fHs3buXWbNmsWHDhpN0BqEhu9QJVAijUnZrhNiGQv3Q4K4DBlFReILNX35Kj6Ejmp1HZfNXnwHQY+gIjLUidVJTLyMu7hzs9qNYLN1bZSJ8J6eIufuOeSUQ/M2tZo0O1cLiI9y6Rx2ZcJen6tdwOiGvZdP6cTTpHK3xm0QTLCEQGRkBOBA9LbcUJEfWDdO26UBS1KJkjiC+uCsP/geTN1zcIYUvWMrs+Ww8/iUbs1fxU/5OjjjcYeytThp6sXnn7fAuhZlPooWlNQilthBAfHw8oIbmgxpJ1JzQ/qZQFIXih29AKi7GPGQY5rMnYjjt7CaXT3wWFsEY2vvfGiG2oRAsNPjYnp2IOh2Z2zaz5evPGfG76U30EsihTb+w9dsvgRrn29qYzSmtdsee43D5xQqo0+uynGIgBlOYgqU9yXG4OGx30stiarTicjiEuzzlq+HkcOSyZ32gc3Rc3DmapUUD0ARLSNisVqAYfSuE584YdAfHyw9R7izj9tEvYjXFAOD22Dnn3TOpqueDtr9oB0ZRnQRzqgrqvHa0eDsGnZlUb9VnSfawJ38dG49/x/bCbewtyyHb6UKpZbERUOhrjeTc1DNJtqYhKRKyLOFR3EiyhKRI2D1VVLgqqHRV0i2qO70Tg6fvbwqnN7rI1AyH3famKcfdVatWYbfbiYqKoqCgoFlJE0PF9cu3FLz/o/pkxR7gXUSjgjnZgrl3OubBwzCfdT7GIWMQ9DU/acWlLk2EYmFprRDbUAgWGoyiMGjCJLZ/9zWr/vMqzupKzvrDZYhi0++poijsXL2C719/BRSFIedPpdug5i/P5ZbZOVJYRc8Ea4Np7A/bnYGlJb2+GOFaWNqL+haiRf3SuSo1vlX6bs7yVNjO0V5OhahLjdZBEywhEG1THVANkrFV7qJvHf1SwLYqZyEO2Vt73UuCQeTi027lQMmfgRI+yFxPF/M93HjWU7z681wW7/0GgAtT+pJvL2JDbgGZT2fizHbS62+9MHc1c/z1bKq2VTDyygxm/fESLuw/h8fmPcP7Wzfj8fzCggULmDJlCuvXr+fGG2+ksLCQvFYKK7S71dTwFktUEy07FqFms7344ospLi5GkiSeeeaZNhuPJ+eo/7G5ix7nCTeyS6A6y0F11gFYeQD4ENHgFTG90jAPGoz7hBpWLhqbDitvLMS2tQWLLzS4vrPtWX+4HIPJzKYvPmH9B2+zf8OPnPn7S+h95igMRlNA4jfJ4+Ho9i1s/Oxjju/ZCah+K+f9383NHtv7G7O4f9kOZAVEARbOHMzlIwOdQHtZTIFuvN4kkSax4wuWYBaiu/cdY3ycrdUsLeHSLOdowOUrmRJCKLnGqY0mWEIg2lvwUECkorqKGFvrT8Ax1nT+fd5TbM5dx7GKTHrH9uPyIfdh1Jv58xnzOb72r+ytqmbx3i+Z3HcWb+z/xr/v57lqLQ3RKNL9ju7YPy7k/KQMJo+YSPclZ7BjQyaVlZXccOatANx555306tWLkpISpkyZwpQpUzjttNP45ZdfGDduXKudU6Gi+r4k2U6tEMVQs9l+9tlnrXbMxnxHpNIiAIwxAglzrkdxuxCtNjzZR3Hs3IHjSC6OAheyW6D6mIPqY4dgzSH//kIIJSxaHGIbRrHAhkKDoxISGX/dHBK792TV0tcozMrky5cWoTcYie0ag2Q8gGiQkD0iBqk/JcfLcdnVbL56g5GzL7mSMy+6uNmRRrlldr9YAXWZdt6ynYzrmxhgaUk1G1nUL5279x1DAnTAhBgb+zg1LCzBLEQScMTubDfB0lznaGe1akkUDZpg6exogiUErOYIZCREdJRWlLeJYAEYkT6NEenTArb3SRrN+zN/4g8fjeSw3cVFn13GqLhk1hfn+9ucl5iOgMCc4XNZtGkpt46a6w+33bHhP3X68yVRM5lMiN6Le1tEFxTp1SWh5Ni2DVOUZZnZs2dz+PBhBEHgjTfeYOrUqaSlqRPnAw88wKRJk3j55Zd5+umnOeOMM/joo4/adEzh0JTviOKdlF2lCscf+xegipeMDbtr2jgdODevxrFxNY4d23EcycGR50CRwHJW0yK0RSG2IRQLrE9jdZROO/d8eo0YyZavP2fXmu8oP1HAiSMFQO3vqGoFtERFM/Cc8YyYNoOohMSmx9oIRwqrqF86S1IUMgurgy4NXZUaz/g4G0fsTnpaTGxcmQ2A2JLCiyeJYBYiHdDT0r5VppvjHO1yeNABOpMmWDo7mmAJAVEUceudmDwRlFeevNDi+mO4su/FPLbtXRQE1hfnEyFCtaz6pUzqMY0LB97qbb00pD4feOABbrut7eobVevVy2FkRNsmu9u6dau/4OGKFStYvHgx0dHRAQkKL730UqZOncq9997bpuMJh1B8R6wXXE3kN9/iKbeDrODI9+AqVVCcDgRvkT3BZMY8airmUVP9fStuF4qjCtEW2vvfrBDbEIoFNkRjdZQstihGX3oVoy65kiN7PmfT2odwVRqQ3SKiXsFgdTN8zN/IGHxRgJ9LKD4oweiZYCXRUUpKRSE5kQkUWmLQCQI9EiIa3CfVbPRbJCzeq2ml3PGdzINZiJ7ul95u1pXahOsc7XZI6AC9qWMnSNRoOZpgCRFJ7wYPVFRWt9sYLhtyH6WOIt46uIJySaHaN0cgsOzAR7UES9O88cYbuFwurrrqqjYaLXj1CpLkarxhC/HVBlIUhdLSUhITE6msrOTcc88lLS2NxYsXExcXR1JSEtXV7ff5BSMU3xF99/6kf/qz+prTwd6hwwE4dvE5iAY96EQEvZ6Yy67COvMmfz+CwYgQQk2b2oQdYttEscBwCFZ/RhAEUnudwZG8Sur7NqT3OztArITqgxIMy4ovWPrN4wiKjIzAS8MvZcKdfwxZ9CTGxQHFnHCfGk7m9S1EHUGsNAeP14fFaNams86O9gmHiGRwgwMq23HCE0WRm89+hjkjXXy84xmOVRxBltUf60X9/y/kflatWsXHH3/MJ5980kYjVbFKBsBJYUl2mx7HVxtowIAB/rwpN910E/Hx8bz55ps8/PDDvPjii206huYSru+IYDJjsIG7AqoO1rX2uXIX07OWYDkpNFEsMFQ2b94ckLtjxIgRQOi+DeH4oNTHV9dG8J6HiMLt2z6id/qfQj4HyWgDitErbvzrex2c2haiUxXJ5Y3OMmsWls6OJlhCxaAKg+rq5hUCbE30OiOXD7s/6GvTpk1j69at7Nu3j5tuuol9+/bx2WefIUkShw4d4rnnnuPmm28mMjKSiRMnYrFY+Oqrr9izZw9/+ctf2L9/PxMnTuTpp59m+PDhLRqnTTECTn8RxLaift6UO++8k/feew9Ql4Fef/31Nj1+S2iO70i3f71O9Xf/Q5E8IEk4Dx6k5IdMFE87hHf6igUuv0O1rNQrFhgKodSfCcW3IVwflNq0Rl2b4qJCAGL1zlNCrHQWFK9gMUd0/KU4jZahCZZQMao/iupqZzsPpHG+/PLLgG0LFy6s83zfvn0BbQYMGMB3333XqmOpFtQkdYLU9lETtfOm+PKimEwm1q5dS+/evdv8+C0hXN8R45AxGIfU1DSq+ugflPzwIs4iCangGI4N31K16mvK1uzAOiCFlLe+b9UaPQE0UiwwFEKtP9OUb0PPBCuiQB3R0pQPio/WqGtTWlYCGIkzdvwooU6FR/3MLBHadNbZ0T7hEBGM6lXQaW9bf4zORJxsARw4w6it0hzq50158sknGT16NFarFZPJxL///W8A3nvvPRYvXsyBAweYOHEi3377rT9Kqr1pUXp2seZufv+42hXABco25RG/dR2mEeeG1FVeVR5Z5Vl0i+pGsjWMcPRGigU2RTj1ZxojJdrCwpmDmbdsJ5KioBMEHp85KCQflIbq2oRTNbi4vBKII9akWVdOJoLXshihWVg6PZpgCRGdUb0IOe3hpLb/bWNHFXeR5rYtyBYsb8qmTZsC2l1xxRVcccUVbTqW9sA8fgamhMU4C9U7e51ZQXLUTJqGngNC6mfZgWUs+GkBsiIjCiLzR81nZp+ZbTLm2oRbf6YxLh/ZjXF9E8ksrKZHQkRYUUL169qEI1YAKu2q9dX6G4xWabbQbQVEr1HMrDnddnq0TzhE9Gb1Ttzl0My9oVLttaxYLW0nWFxVVbgrypGcTmSXC9ntRnI50RkMxJ02uM2O25HQxSXT64eduPdsRCotwnTWZLIvPYeKXcV0uWY8YmxSk33kVeX5xQqArMgs+GkBo1NHn5QJKNz6M9BwHZyUaEtYQqU2vro2zSHRph6zoPq3lSq+vYSuD8H7duu0TLedHk2whIjRrEMBPI6ACiIaDVCtUwVLW+Vh2fnv11nx1f+QxeAm+BG9BzLhsafa5NgdEcOAkfiM4opHFdaCITQzeVZ5ll+s+JAVmWMVx07aHXM49WdCrYPT3JwsDdFYRuJoq/q8yhPaklBbFB482bSa0A0jU3J9fIJFr9eW4jo7mmAJEaNZjxOQnL+tu6eWYNd5E8dZ20awHN38S12xoiiICigCKIJAwbFMinfvQh8RgTkuDmNU2y5NdSQUSX3vhRDqCAF0i+qGKIh1RIsoiKTb0hvZq30ItQ5OS3KyBKOpjMQur0g0iE1fI9qy8ODJpFWEbjMyJddGQK3BptdS83d6tE84RMwR6oVQdmkqPlTsXkflKFvrX4hlWWbviRwAMqIT+Ovbn3DXB1/w1w+/oF+Seod23FnNGwvuZcm9f+HlP17JgY8/aPVxdDRc23+k+JGbcOeXAKFVagZItiYzf9R8REG9JPhM+yfbHyEUGquD46OhnCy5ZfaA/vKq8vgl9xfyqhou+tlQRuLKkpo0Bx7JJ1gaH39DgivH0foO/TkOFz+UVLRJ31AjdGsTltBtKFNyWei5m3wWFoO2JNTp0T7hEDFb1Au/ogmWkPA4HTi9qxG2qODp11vC7rff9D+urqpE1NcYCzPGBNbOkUWRnK2bW30cHY3s2/5M/jtrcVWo74doC92qNLPPTL65+Bv+PeXffHPxNyfVDyEcfHVwalO/Dk5jOVlqs+zAMqZ8PIU/fvtHpnw8hWUHlgU9ZmMZiX24veG1hiYuEaEIrtbgnZwizvhpN5dsPcQZP+3mnZyiVu0fWkHoNpYpuR65ZXbWHyoMEJ1+Hxa9Np11drQloRCJiDADHgT3by8CoDlUlJ/wP7ZF1y1KV3b4ELvffwdZ8iDqdAiiiCjqEHQ6RL0BY2QkRqsVky0KY1QUhshIdDo9ol5PZW4u25e9zx6vdQVg+hPP1um//5XX8OvyZeTXKwlgjmqbopUdCU+5es6mRBORw3vVSdUfCsnW5A5pValNKHVwQsnJEo7/RSgZid3eHC5NWVhORuHBUJfNWoOZfWYyOnU0xyqOkW5LD+/7E2Km5MaW93xvt1FbEur0aIIlRCKtFqAC0a3F+odCZbl6N6f3gMlsrfPaikcf4mh1WYuPYZAkLrljHrb0QL8Ep8cNAnQRjVgsEZgjIhh07ewWH7Mj4sk+RNWy11FcLmSXup6ftugJTGdNbXLfU5Wm6uCEkpMlHP+LUDISu70+LPom7mlaWngwFGfdxqw4beHg22yhG0Km5KZKLvhchgxG7Ways6MJlhCJtEYAFeg8mmAJhfJyNU25xR1oH6+yVwEQi0iE0YyiKMiKjKKoE4ZHlvDIMpIi4wEkAWRBQKmV7jwjOoFz595HbN/+QY8vKwoIUOhxIJQ7sFSUIrs7Zw6d3FuupXJvifeZ+h6J0S1bhgtWiLCj4HDkUm3PJM7Sg9TYhjPfNpWTJcETSUqRhbIIF9UWVWw05n/RVEZij6TOnKHc6De38GCozronw4rTajSRKbmpkgt+C4u2JNTp0QRLiERH2oB89J5TM/zwZFNZpU6gEZ66F5Gj33xNkewGQeDMqRcxaPackPtUFAXJ40FAQVevCrEsy8yePZvDhw8jCAKXJUaz+sAhdhzPxaTXc/mZQ3lt7p+54cmXiOzWnUGDBvHnP/+ZW28NvcJ1R8VdqBZBFPRg7RWFuW9PDP3PaHZ/jRUibA08ZU48hXb0CRb00eFNoDk5HwQUQUxNvazB9g3lZNmx8ltWvPYSU5QkZBR+GlzMoW7VTfpfNJaR2O2NzDLqQvNzC7fwYDjLPC214rQUd14ersyjGHt0Dy2vTSOZkhtb3pM8MqJXpBsNmoWls6MJlhCJtkUCoFcM2B0OLOZTo4R8e+ETLBa55iKybM61HKko8ReGM1qtQfdtCEEQ0DeQV2Tr1q04nU7WrVvHihUrePu//6VEn8NPy7/g348+xBe7D3Dx6YNZcu9f2JuZjVhcyPp//RPjmq9RFIXk+CR+/+obzTzb9kXw5p/oes91RF4XvChmqIRSiLAlVG3Mo2TZAbyRqMTO7IN1ZGhLCQ5Hbi2xAiCzZ+8DxMWd02iNofpUFBWy4rWX/OcoIjBmZwKLrn+SXukDA9o3lnulNi6vYNE3kBcoVHwWpAhLjzrnFe4yT3OtOC2l9KOPAkocxFxySbP7a2x5r7pW5nGDUbOwdHY0wRIiUZE2/+PSygpNsDRBpb0UgAhZFRiyLJNVVqQWmPNijIxsteN17doVUCfY0tJSevbqhS0qipRRo7lj6Tu82revdxwKPx3P5bT0FOySRIUIIHC0KL/VxnKyEbym8OynlsLTS0HxOocqEH1GV1LeDL2oZaiFCJuDp8xZI1ZQx1ey7ACmvrEhWVqq7ZkQMGXL2O1HmxQsta06Jbk5AeeIomAoD6x51VTulTrH8A7NEKKFJRiNWZCas8wTrhWnpbjz8mrECoAsk/vQfKxjxzY7gzA0vLzncte8GwZtSajTowmWEDHqDbhFFwbZyKr9W/jHF2/hktUQSQHfBUqo9xwMopkHRv+VKX2Gn+whtyuVjnIAIry5V0VRJF5vokBW74gSRQNdx58Xen+lFax683+4qu3o9DocVTIVxR4mXHsBvc/oTkJCAqIoMmDAAJxOJ2vWrOGyyy5TrS6bNiNbI5n14OP887lnueaCC5EAu8tFVFUx5SL0TAz9Dr2jYe7eBfvxo8hBMqxWbDtOOGfWWoUIg+EptNeIFR+Kuj0UwRJh6QEBU7aIxdK90f3qW3Wsk1MCz1EUiUmuK0Qayr3SbWBcUEuLb0mouflAmrIgtfcyTyi4Mo/WrXgNIMu4jma1SLBA8OU9Xyg5aEtCvwU0wRIGLp0Tg2zkta3PUmrNDjmLzdz113HXD3pUKSOCIqKKG7FmGyICIoIigiAieLfXec33X/DtI3j3F/wiSfDmQ/Dtj+ATUDXtBAH//mpfQt02Qo3wEgR1hbjmmCDWeiz4x+rrS91WUr4dTGCVyqh6ciboDIyIzSJfljGIMh5ZJOe/tyFILpA9iEjq+Qhi3f+iiCDqOLZnH1FVFaiGA4EoBZzVMXz2zM/c+e5SvvnmGywWC3v37mXz5s3cc8893HzzzUyePJlhw4bRr18/Ygaexvr8E3z22We89dZb5O3ZSUrRXrqKCsO7RlO99H7VEUQUQdR7/+u8f9VxIOpQBBFBpz4WBBF0OjW6QSeq7QU1VBudHsXtAkc1srManNUoLgeCIgEyCAqCoANRRBHUfQVRVPsS1XNXH3uP4z2++prO/z929o1EjtutftkMRgS9EfeR3RR9/D06M3Dkh1rt9d73Ve8fq+/8EPREi3pmTD6Hr75ZgQQo6Jh2wUVEt0JIuD7Bon5la4sWwbs9BMzmFAb0fyzAAtGYdSWYVcf+bS6Tr/sL3761GEWWEUSRSTfcii2+rqNyY7lXggsW9W9zBUsoFqT2WuYJFWOP7up3qbZoEUWM3etG8vn8o1qKy1VT282kLQl1ejTBEgZub4Yig6TeDcYwjKndf+e/FtYOkVQUhR+yfyBPXg+AIAaam/1t6/1tc5QGHrcm3hvmroZyrBXfA3BaEpxWu01OYHKohkiNBOqtIFW4jbx2sMYPJjZWLQEQExNDaWkps2bNYtasWaxevZqkpCTy8vLIz8/nggsuIDs7G0feId77g8jpqTpw74cjzTjPDkIw+0TMNO+DpReE1ddQ738/n78En4eyZ60JqPZk5P1d6A0RpJmdXsEGkhKH48KfwnK8TU29jLi4c7Dbj2KxdGffjx7+98hK/+vJhr3MiHsInU41p+gQSDX6xqbeJCiIpG0zMnC4Ts3rYbQiJgcmbQsl90pt3F6v0OVHYNtj/8QlC7gVAbcsoBMU0q0yJr13HIIABbvVdyymOxgiUBQ31dUzvCP1mXUEVm35GVH8FRSFbk4bGZ4uCJY49gP7G3uzvB+Bf/iCUOtxzWuiAjpFwa0TkAT1hqZ+u/oowcSGdz/pyudwH88BQb3hMXRJJuudoyBnggKJ1RJJdpkdSUZckXoEGQRZLashKIr6XAFBJyAaRAS9iKgXEfUCgl5EEQXvxylQVVmTa2nN22u8b62AKHpvwPxvt/eGyrufIAgI3n78N2Si+rn4b+rEwPaC4G3jvREUdN72vtdEAdWFqWZbwH6iqP719ivqhFrtvTeBtfcR1fOp6UNAFEUQ1ZtHtY2o3kiK+N93Uae+JgretqKo7ncKowmWMFBQ/ViuSnuYCydnkBjZ1F3nteRVlFBYVYFblnBLEm7Zg0eSkBQZlyQhyR7csoRHlpC8bTyKhCTL/m0e3+uKhKzIyIoa8qugoCiKPyxYVhQU1McoIFOzTW2joG5VfUpqtsve8/OGF6P4Q4zVrTVhx+pr3ja1jl/nOQq2om0Mc+3gvDIjVUo/rMK+oO/QCSUZR2R3ZLzmXO8xUWQERQZFRpFlFMmDbLfTy1QjcuySgdOnq4nRJk+ezFtvvcW5556L0+nk2Wef5YorruDEiRN0796dl19+GYvFwq+//grAv/65mPJP7uX0VB0ujwnJE4OA99jI3glDfazadHzPa8tLxXs9ryc5BaVmwkFAUVQrmSKIgA5FEVGUujWQEBQEn+OJUHMcIdgxhZrHAIKg1PQlqGMSdDKi7mTXvap1vPqmCQB3dZ35T9RXYj296UrS9TGbU/DYYyk8aqe88ESd1/Lc/amQEogRcgH/vBaIs8bGif0EfPsgDJxep4k/90ptH5Z6uVdqE2VSJ4MDniQOVAS+/ktAVQDvElSdBLQ9ALABMYjEInBapY40RE5HT6rPrFvVNqn2WwcLxGbUPHUAQUoDDC5wQUHLzsMhK2R6H+/+8aTf+p2yKEr96xlNPlejM+1c/8TvsERG0B5ogiUMRIMOXDJmLCGIFZVkWyzJtrYp/teR+fn9Jzhrzw9sjhyHfPGjKP85N2DiKJGiib1/I/qI0N7LzJeuhCJVsDglPba5PzM+Qc2ZodPpeOedd+q0Hz16dIN9TbYdI/1sPRVSBNaHj2LUdyzTeov54Xn4bj7ojPC3E8HbKArIEsge7193rcceUHyPvf/txbD9A3BVqc93fdz88cX2QLj2U/A44PBqtb/aRHaBbmcG3bW2I2x9zox8lxh9bs2GYVfjsidh35WPoLgQBDfmXhEY4vUguWD3J+BxqucUhL1JG/jviOdJKYrm9AM5FC8fQf4PfbCOHUPkmDF12v7lyt+T8f1KJFnBqNdh0OuQM4/g/PFHXDodBZFx6DIyMCQkAgrK0Z8AGZKHophsoAiMOmwkla6INO6PcShJoDxKtS7Wc5Gu0c14tXD9nRXfa76GApIIBo+sWjYCO/X3V3NApdZjAh4LouBd8VatDvju/kWB5Bw7cZUeKk0iedEGtVipKCB7i5YqondhTFLUIp6SApKCICmIsoJPhwuKgtkj0ccpUuyRKNeX1BlH3e+HUPutqdVOqHM6tc9UUeqcdQP9CPX+1rRp6HUlYFvTf1tj+aw2gs/0FCaSx8yRLYcZeM6gVh1PqGiCJQwEb5EQp73h5R0NFdnn76JI2HoO41iv2aQfeYMqOYLS+DMxnXkN8Wf+AUEX2lewKucAPYq+BMAu6VH+/DMRCc2rJKzIMtZ974MIpd0uxNbZxAqA5F3iEBoxAQsC6PTq/6bY+yV8eL06yYeKLUW1mPn/K5A8GH7/MsR4P7unMqC6MPj+Y+6ASQvqbKrvCKueB0y4uh99z0hCv3oFbKjlKLP1bYxzvkOcNiR47peEPrDy7+AsDzi8P3W/UebJZSdIKAf4gWJ+oHjpUvqsWY0+sabsREx8F6667Er/c3deHgfnza/rz7F+Pd1fexnB6Ubo0wvDaaehS+wCgJSbTe4LNRZEgWp0ukpEgxOXo+a7HpFwkHF3XK/6O53iBE/7GDonthzE+X4uDtlB76eaHzp9KiDLMsgKMgqypP6e1J+V1+otea3cstdyLssgU7NNltW2sqLaTiS5xkIuq9YUWfL+lQFvH6oxRuGLl/eAYFFv3NsJTbCEgRo+KuF0SE22/a3jqlTvdhSTGgqbfv3zeMofwBoZjzXMC+3xd++n675X/M/lG9ZiTend7LGd+OEdksQS3LJIwoy/NbufDo3bW0VYaIWLy65PVLGidggGM/67sy6D4Kr3wRABR9bA+1eD3gwXPg+Dm5hANi2tJVZq3+35xMY7AYIlmCMsCkQnRqA3G2DqQpj0KDxaK/vru1eiv/tgcF8Zc4y3j/rOrnVT95vq6TRT/37o6oV5V1XkIxSWYumSjhARETRixpycSPHnOvBZUL7ej637tzjy9LidKf7tqXf3Q4yvu1zm3rsLT3Yu5gmdQ6y0Dur3pn6Jhc6I328F2mnm3gOArqnaE22IJljCQPSuUbs0C0vTOMoAUMw1F3V9VGJDrRsn80f/w9yuM0npdlojjZvGvW4xAPnW4XRNDKxD1CnwWUIas7CEys//rHnc7WywJtXoC0VR68CAarHpd4H6d+/nsPeLWg4kXkdTRYF9X4LRCk6vo4clDu6t5fG8Zzm8fw1UFcCr41XrhyCCzkBi9ykIwrjGHWF1enioBJ7sru5bdQLWPQfn/DXw3IzetfggE163qG6IgoisyPzxDp3fgfGbi78JyIb79ld/omxbCheVTgDdbtLmT/NHzAh6EWNsDPrYOEzp/ahfIKLiaA//Y72ugIgMJ2L8OQHjMfQ/DUP/ln33Oxs6kzqF6ZpYQtNoDdTfsl6zsJwa6LzFtTxOzcLSFDqXamJXfHewLUCUVGtBjr4vSVe/1KK+KrN2kuLaAwJETL6nxWPrsPiWhMRWuLjoamUXzvqp5f0BuKtrHkfWc7wdMB1ie0LJEcjdUuclU8Fuxl9zY6NFCAFVqHQZBFlqlB57Pg0uWKK9glWRYOl06D0J54FvEfJ3EtVjDPNHzWfBTwsweUzYPDZuOPuGoKn71xVs4gr77eoTKYGip9/nUDeBry67FIA5jvPV0/a21+lOoDdX4bHbMMWVYcqIxjRiKPrugUJFo2F0ZvW7qRO0Gm9tjeItgqA3tJ9s0ARLGOhNmmAJFaNbtbAIlpgW9+ULg0317Cfn2fNJfeDnZvdV8tmjRApQQApJwztvNWM8XgtLaywdnPc3+N/N4LbX89Pz+Yp4/wZ4L9b2TqwXQSTqodd40JtgbBAhcfVHsOQ8dfwpQ1VrTPYmoOkihHz/CKx7pua5JRaueDf4ufU8B1KGQe5WOLIWjqz1h4gb93xORJ9zeL7P86z5dg0osO+TfWyWNgfUVqqQ3Yi1EjM5Knryy8Ef/e+XjIxBV4ghohK9TcFyVl9MZzXsFK4RGqLRa2ERtKms7VG/3/p2rIqtfcphYDDp1DwDLk2wNIXRo5r79RExLe5LHPUn+OEuAAyeIPGiIeKxVxKfvxJ04B56fdM7nMp4fE63rfATTz8Tbtvc8n7CIaE33J9V8zx7CywZ73/aWBFCfv13zeP+F8JlbzUu3GZ9AQsDC+85BHh087+YnH1hrciTerWVqgqhYA9XJ5yO8Ujdu/whUhKr9Oo5uJGI7efGdl0n/96dZCSXarOS0a7JbY/6G9LpNQvLKYHRoscJKK7O7+DVUiySV7BYW57SvcvEORw59DM9cz/AY4xpdj/5y58mTeeiUjKTPO2OFo+rQ+PzYWmNJaH25psHVAfdMFH0ZqorqjC+fRUGk0m17ESnwcQFUDsyrCow7NsFmBRYnnOYFxWJ2mmt/bWV9G54ujegMA047qorRjI8fcjw9PE/lzp07pRTE8ml+hP+Fpxu2xNZlv1Z1PVGTbCcEph9gsWjJSZqCqtUCYAxsuU5aHI+e5qeuR8AIHuaf9E37XwbRChJnUykKbR08KcsktdbojMIli1vgctrWRND8FXwthE8DqzZqwJfLzoEw6+F+AzoMhDiesLM12HZHH8Tn5yJkz0M4BBJnEBEYSf9KRQSSHAfh7WfU3cJzFVrz0AsI5of2aYRHNmpWVhOBh5Xjau4ThMspwaWCANlgODW1HxTWJUqEMBsi2+6cROYf33Zf4ObJh0g55MnQGdAEHUIOgPojAg6HYLOqG7TG0GnR9QZEfR6BNGA/dh2UsQTeGSB+BnzWzymDo/fwtIJfuLnzIUV3vDzjPGw4Z+q067boToXS0418ZzOCKIBT1Q3dJUFQI2HTZ28Wwe+Uf8DdB0Jc76DIZdC5lrkLW/jUSTy9Hq6edS798tr1SU4l18oTTwb2zvP1h1jdDdEZzWyYkQQXChKjXARcBI/RcJ01pTWfV80kNzqZ6QE1GBqXdx5ebgyj2Ls0b3FRRxPRTzOmshYg6H9HJw7wdXs5GG1eu/cJM3C0hiSJGNDzRwaEdXyJSFRqRtGnrp1Ydh9xHj/5lsGk9aCHC6nDL7MrbrQ6/R0WMbcBtvfh/ydcGCF+r8R9OB3dhWAcreRCJ0bvVhTLsFvGTm+EcpzISoFLnoJ8aKXKK7KI7/kEOmvTwuaCzSmYEPgxrIsUFRrVuI1KSB7UNwygtWKvmsqotUauI9Gi5F9S0JtKFhKP/qI3Ie8CQBFkZRHFhBzSedOUlcfyVNzDdabNAvLKYE10htCp1kfG6Wy2k60oF5ArLaWV/mtGnELjh3vqdFC3hpDArX++h8rCMiI1DwWUBBREAQFl2LAeuEjLR7PKYG9WP1rablg7BBc9ym8fCbYS9VYZrl+NpOGEUWZhVmjueOZN2sqMv/nQshcpz6W6wriZEsSyaIZJjwIG5eoy2u9JkDBLij1OgILIrgqa3aKTkdxqIJFtNnQd2teFmaN8PAJlraysLjz8mrECoAsk/vQfKxjx/6mLC1uVy3BojndnhrYIlUzr17WLCyNUVFVhS9dnKkVfEXS/nA//OH+FvfTCWwNoeNN3Ie15UtyHQJrAtxTq7r36xNV60gIROo8XN7nF+S3zgVTjJokrmB3TYPnB6nZeWWPuvy09b9QdlzdNvRKmP588I7z98KyP0LxYZRJj6H817sMZGwgeqkN8JQ5g5cc+I0guyVEQBba5pocLFsxsozraFaHESwn4zvg8QlDRUbUMt2eGkTZ1AuSQQFFVtQCXxoBOB21ytJ2hiWJUxGfBcLYSZci5nwH710Lez8LqXlftxtKjwPHgzfweEsZrHmi7rZNb8AFzwYPi+7SH/6kZmEWJDegLhXJxQWQ3MyszmFQtTGPkmUH/I46sTP7YB3ZMSbRk4Xs8iACitA2FhZftuI6okUUMXbvGBmyT9Z3QHL7LJrt67+pCZYwiI5SJ18BgapqN5GRnbBoXisgudSLvwcdeq3mSfvgq36s78SC8bKlarXlygLAW3lakQGFrPIsvtj3AeOr7VR5PW4FoG9sH2yGSHBWqks8oJYTOLxKFSiCqIY/K7XWfR+Jg9u3QWz3hsciuVDz2BoQo2La4mzr4Clz1kxUAAqULDuAqW/sb8rSIrvVz0kJVl66FTAkJ5PyyIIAH5aOYF05md8Bjz/QRBMspwxRVgOyN0FxWblTEywNoHjvVl0YtC9Ye+Hzy9CfvOWJk44owqCZQV8yVuXxzxPf8UpszQVWrQO0DJs1GY6sg6UXqi/kbIIR18HvnqzbycO+hU0F1jwJM16hQWQPvlA2wdpyv62m8BTaCZijFXX7b1GwBPWObiViLrkE69ixuI5mYezerUOIFTi53wFfNFbgAU8u2nwSBga9DpcAZgXKyl2kpbb3iDomklvNsurWvl7thy+RVmcWLI2QbE321wGSFRlREJk/an5NHSBLrfxAFXlqgcesWtE/9Rxx2fFho4JFkRX8FZhPwlKxPsFSJ9gJAMG7/TeE4pEAHYrYthOpITm5wwgVHyfzO+DzYRE0C8uphUcEJKio1LJWNoTsEyxaQbL2w78kFL5g6Sw5J2b2mcno1NEcqzhGui29btHC5EEw9k61onThPnVb7taGOxNE/5JAMJRa5ToE3UkQLNEmYmf2CfBf+C1ZVwAUjwzoaici/s3w/+2deZwV1Zm/n6q6S+8L0NCAICIKKCKIqIgKIogR97gbDc5odNxjMG5j0DEJGo06o8kozERj1NFRiaP+3AVcUZDFICiiomwNCE3v3ffeqjq/P2rp2zRLL7fr3u5+n/7059Zeb51b99S33vOe9wR5D5im58kSwdKpsHQNLKgRwbJbbLdJKIEIlrTheVjCrRMsXS3nRGlu6S5HVwZg8kzn/6WrG+NZ/AQu7mefQ2C/Y5w4lz3EY2lZuYDztm9t+AF92EGpuoTdkju2lOiBxd27l5BpAWFUB/USynSCugekSaiTYoc0SChqa1ueB6K7oVwPi6l1jRif6upqJk+ezMqVK/nkk08YMWIEzz33HA899BDZ2dn89a9/ZcCAAUyfPp2VK1eSm5vLtGnTuOmmmwB46KGHeOWVV0gkEjz11FMMHBhADwO/Sajl7uFum3PijD+1+xCqrhqvScgo6dXu47WUUGG0WwoVD8fDQrf0sHgEcQ9YbjlraRYs3fhrbhsq5Lx51deZe9kyeKqrqznyyCPJy8vjiy++AOC5555j3LhxTJo0ifXr1wNw/fXXM2HCBMaOHctzzz3X5BhXXHEFZ7fzjVqZnmDpGh6W7OxsXn31Vb9cEokEDzzwAO+99x533303d999t7/t448/zoIFC3yxsmTJEtatW8e7777L+++/H4xYgSQPS06Ld9lTzglhz9hb17tTcfSevdNqS3eiUbBIiomOxPewpNmTJYKllWiuYGmoyzwPS0sfrPfffz/vvfce8+fPZ9asxjT333//PWVlZe22wxMsXSWGJRQKUVLSmFdjzZo1HHzwwUQiEcaPH8+KFSsA0DSNyy+/nClTpvD5558D8H//9380NDQwadIkrr32WiwroDTJnmCJtNzD4uecSCaDck5kMqq+DgBdy7x6oSvjCRYlT7IOxXK7NWsiWDoXWsRx+zbUZ56HpaUPVm/wqtraWg46qLGt/d577+VXv/pVu+1o9LB0jSahnamoqKCgoLHrqidC7r//fhYuXMjDDz/MFVdcAcCWLVsIhULMmzePnJwcnn/++WCMVG7F0oomIS/nhC9aMijnRKajYk5Mm6aLYAmSDV84ddr2TT+k2ZKujT+WUJoFi8SwtBIj4lTmiYbMEyw7s7sHK8BFF13EO++8wz33OJk9v/vuOzRNY99995Acq4X4gkXvmoKluLiYqqoqf94wHBHbs6eTBn/YsGGAU95FRUUcffTRAEyaNImPPvooICvdiiXcuky3mZpzItNRMSdpnKZnfr3QldA157dnq/T2Xunq2JZ4WDoloajzA0nEMn8ExN09WAGefvppvvrqK2bNmoVt29xzzz1+3EW76eKCZciQIaxatYp4PM5HH33EyJEjAfyy3rp1K7FYDMMwGD9+PMuXLwdg2bJlDB48OFhjIy2PYfEIl5aSe+QRIlZaQaOHJfPrha5E6X4HANBjgDRbdiSNTULptUM8LK0knOU89M1OIFiSH6yLFy/2H6yxWIxoNEpOTg75+fnous7333/Pv/zLv1BfX8/q1at5/PHHufTSS9t2Yrdbs9WFmoROPvlkli9fzurVq7niiiu44YYbmDBhAllZWTz55JMA/OxnP6O8vBzLsvjjH/8IwLRp03jllVeYOHEiJSUlPP3008Ea3oqgW6HtKC+xlgiWFlO9fRs7yjZR3Ldf4yjarURzPSxGVB5lHYnl5mFJt4dFvuVWEskKYQN2IjNdkC15sF544YVs376dRCLBHXfcAcBbb70FOIG3M2bMaLtYoTE1v210HcHy2muvNVt2/vnnN5l/+eXmA/EZhsGcOXM6zK5d4nq4gK47+GGG4QsWQwRLS1gx7y3env0wSik0TWPKL67lkEkntvo4Xh4zTdoKOhTLEg9LpyQrJ0wdYMczU7C05MH64osv7nb/QYMG8cILL7TLBpVwBIuSkZrTQ7y2cTqalz47uhFefaAZmVkvZBLV27f5YgVAKcXbcx5h0KGHtd7T4ha3km7NHYpt2oCO1sFDIOwN0aWtJC/X7aqboR6WTMBLHGeLYEkPyYJFPCyBoNw8FVqoe2ZcbQ07yjb5YsVD2TYVmze1/mBeNRzAcAjdmUzxsIhgaSV5ea5gMaVi2i2WI1iU0T0H3ks7yYIlJDEsQaASTn2gGVIv7I3ivv3QdnryabpOUWnrR5P1QiqkSahjsb1Mt2kuZ/maW0lBvhOXYVhSMe0WL4YiJB6WtJBIEixJPcOEjkN5vSjC8qa/N/J79mLKL65Fc/P9aLrOlMuvaVvgre2UtzQJdSx+t+Y0KwaJYWklRQXOQzgkLUK7RXODbtsyUrCQAuJ16bag2+F7WELy4GwJh0w6kUGHHkbF5k0Ulbajl5DnYZFybxE1Oxqo2FpPUe9s8opbXj9bbouCnmZhKIKllRR7gkVBwrIJG+Kk2hnNcnNSdFIPixWrp+zfTyXU8CNKC2HrYdDDKD2MMiJgRPzPUL8R9DvzthYPkHjNNdewfPlyTNPkrrvuYurUqUCKB0hMeIJFKvGgUF4iUHlwtpj8nr3aLFQ8NOWWt3hY9sqqjzax4KmvUMqJRZn4s2EcNL5lzXC2nRkxLCJYWonnYTHQ2FETo3dhy1Ofdxd0y/Gw6OHM9LDsTVz88YpTSKz5hDOfq6MmrohbcNL+If5+fg5LNpmMnVPHmH46+xTo9M55npW/fw4jp4jPP/+cM888E2gcx+mDDz5g8eLF3H333cyePZsbb7yRwYMHs2PHDqZOncrUqVObDJCYEjzBku7apRvhCRY9LC8wQeJ7WCTodo/U7GjwxQo4I3csePorBh7Uo0WeFtsNgdDTXM7t+nXNmjULTdO44YYb/GVz585l6tSp9OrVC03T/Cyfe2Lu3LkcfvjhFBUVkZuby6hRo/jb3/7WHtM6jGhWo8bbURnbw5bdF912PSyRzBQsexsk8o9PvMgR/Q0+uDSXylsKOPnw/VlRmcv6Pqdx5TvZ7FeSy6wLjubAniFOPiDM3x+8jaeeeoqjjz6aaNQRtLsbx8nLdBuNRtHdNvyUD5AYFw9L0CjLKWstkj7BUllZydq1a6msrEybDYHjeVhEsOyRiq317NQxC2VD5db6Fu3vCZZOG8OyePFiZs+e7WdP9aitrWX8+PGcc845XH755S06Vo8ePbj99tsZNmwYkUiEV199lUsvvZTevXv7LvNMQTc0nB7psKNaBMuu8ASLnsYYlm2L/o/6Rf+DQgNNQ9MN59emG6A507VrPmHz33/PFsNgULSWrf97O/vqBl9+/Q0cCYOLdUxbseK7TfTabyTmSf9G6WsVqLIySqf/J+ULjqQkFxLb1vLU659wyoQjWbJiJXVla6iubSBbt6jb/B2aEWomQm6//Xauu+46wBkgMRqNMm/ePG6++Waef/75ZrlzWkXCrYTSXbt0Atrapr8znmAhlJ4g56VLl/LKK6/4idhOPfVUDjvssA4/7/rN1WzeVE1hj2z6luSSnxtsskivSUg8LHumqHc2mkYT0aLpUNi7ZS0EXtBtuj0sbRIsNTU1XHTRRcyZM4ff/va3TdZdfPHFgJMxtaVMnDixyfz111/PX//6Vz788MOMEyyapmHpoNtQVycDne0Kw/ZyUqSvxTH0yjUMMKr2uE1ubT2lZRuoiin6xk32+WY9AEZDDeAkXJu31iKMzXXXXce9997LnXfeyZQpUzjzzDPpZ5oc2T+KsXwWrz1Vy/PnZPOPb2Lk/H0e+RrUL4mT8+hLANhlCeq3/oDVUM1Tc1+joa6WCy+8ECD1AySaGSpYKjdC+bfQY38o7J9ua9rVpt8Md/A9LRROoYUto7Ky0hcr4CRie+WVV9h///0pLCxM+fkSCZtFn22gftFmDiyL0dezA/g+olF3+n4cOSaY79ePYZFYwj2SV5zFxJ8NY8HTX6Fsp2qYeNGwFot0b2zJThl0e/XVVzNt2jQmT57cTLC0F6UU8+bNY/Xq1dx777273S4WixGLNXo4kgf562gsHcI2WBma7Tb9eAFa6atEcrUaADZED3W6Vyvbeb1QFiiFpizq9K/ZEupLKKRYX7+Jw/6rgdU/NjCoOMJmbV9ueGk9L/6jjqEHDuXII4/k/fff55ZbbqGuro7DDz+co3Jz+c8la6hLKJZssjjvhXrWVdr8clyUA3porPrRJm4pFm+0GNPbJPvPI5m/1uTVhXGeOzefytWXUjh0HOPHj2fZsmWcfvrpqRkg0Uo4n5kUw7L0SXjlevza8tR/h8MuSZs57W3Tb4btHsgIXqSXl5c3T8SmFOXl5SkVLBu3VPOP99fRd+UO9m1oPN+mXJ2CmE2eCcVxxYZV2yFgwSIelr1z0Ph+DDyoB5Vb6ylspUexsUmokwmWZ599lqVLl7J48eKUGlJZWUn//v39UW7//Oc/M2XKlN1uP2vWLO66666U2tBSPO9vIiYell3ReEunR7AkqrYT1h3R1PtfXiBS1HuX2+V8PZ0+v5jB0KFD+fUxx/DKp3O5/PLLyc3NpXTm85w57DnmXnwxDQmT008/ne+++w7DMMjKyuKzzz5jXlUV11xzDVu3buXIY8pYtmwZdXV1nPJqEXfffTc33J/F+N/fSV71Nzx5huN6vfL/NZAXgWlPVaG9Np35S1enfoDEsOvmVRkyrk3lxkaxAs7nKzfA/iekzdOypzb9tggW5QmWNHhYevTogaZpTUSLpmn06NGj3ce2LJvFyzZR9UkZQzc0cIi7vCKisW5oIcMnDOSIfRxR9OajSzj4+zr0ggCbhSwFWrCCJRWDNqaLvOKsNt3ftp0ZQbetEizr16/n+uuv56233iIrK7XxCfn5+SxfvpyamhreffddvzfFzs1FHrfeeis33nijP19VVcWAAQNSatPusA0NEopETDwsu8StONP1gl+/9XvCgGnrhAt2XaHsPEjkL3/5S84++2zWrl3LU089BcCMGTOIRqNomsaAAQM46qijmDp1KldddRVVVVXk5+dz1llncdlll7Fp0yaGDRvGjBkzeOSRRzjjjDMoKipi8qAovd6YTnVMMWZOAxsbsvl/55YwoXc5/1l9GOPGjWvW7XnSpElNuj3Pnj2bZ555BoAvv/yS2bNnc/rpp+++AHJ6Op92hgiW8m8bxYqHsqD8u7QJlva26TfDu+fT4GEpLCzk1FNPbRbD0h7vytbyWpa+t46SFeXsU9f43a0pCaMf3ocjxw1gRKTptYYanPstnBeMaNuwYDlZmjP0hBYK5uUoVYM2djY8D4vRmQTLkiVL2Lp1K2PGjPGXWZbF+++/zyOPPOJ7R9qCrusMGTIEgFGjRvHll18ya9as3QqWaDTq98gIGmVogKK+IZGW82c6Gp77MPWViBWrp+yhk8mqXY/SdJSmoymLEm0r1VY2MS0PDQt0+LEhzBnjxu22+/Knn37aJDeKUopDDjmE0tJSnnjiCbKysujfvz95eXm88sorjBo1itmzZ1NaWsqll17KMcccQ48ePVi0aBGzZs1i6tSp3Hzzzb4XBqB49ElseP9wlNrOc8/cyW8ffwl4n4SlmP3Kh3z6xbd77fb8i1/8gl/84hcAHHrooXv0PAJJHpYMycbcY39HDSSLFs2AHu1s+moH7W3Tb4ZX1KG9excaEiZZ4dQKm8MOO4z999+f8vJyevTo0SaxUlkXZ/Xqbez4ZBMHrqtnhHtN1SGN74fmM+TYgRw/qHi3++veQy0cTOBxxWfrKMKxZ99jD+/w86V00MZOhh/DkuZYoVb9ak444QS/e6bHpZdeyrBhw/yKOlUopZrEqGQSXnIoCbrdNZ5gUR3QJLTpyWsZUL+0aWuTK/rzjXryaeymZ2eV8Oqrr3LTTTcBLcuNcsABB/j7X3/99eTl5VFTU8O8efOYMGECuq7z6KOPNtmuqKiIM888k7PPPpuamhpOO+00X7AY0Wz2+XVSfpXHXwJgTbnN6Nwt7Pj9KIagsfT1tWy+ayE5aJRpBvUjL/G7PXssW7aMoUOHkpOzl/GBMi0PS2F/J2bllRscz4pmwKkPpT3wtj1t+jvT0iahjz9Zx8CXfmDxMSWcecqwNp9vVxQWFrZJqKz8ehuVc9cwsMKkH+CFHX/XI4w5pjdHHj2A4dl795oYvmAJ6KHmnmZHwXb2KSzo8NPtadDGri5YbF+wdCIPS35+PiNGjGiyLDc3l549e/rLy8vLWbduHZs2OSNvrl69GoDS0lJKS0sBuOSSS+jfvz+zZs0CnHiUww8/nP333594PM5rr73Gk08+yX/+53+27+o6CC1iABYNteJh2SV+k1Bqb27bjFO87lUwYGPWoWgHnYqyTHKX/pkio4ptdgmJI69BmTGUZVI87jxySkr8/XfOjTJjxgygaW6UZJv//Oc/Y5om48eP5/PPP+eiiy7i22+/BZz73qOqqopf/epXLFiwgMsuu4zNmzfz5ZdfMnz4cH+beNU2dix6CduMY4aLqKjZQkFUow8bAdDtGKVqnVt+cOU9/8Z11/2pyfU///zznHPOOXsvqHgG9hI67BInZqX8O8ezkgG9hKDtbfrN8J5jRpgPP/6B+Keb0S2FvVOQ4oFbnS7/Yz/8EVIsWFpDLG6yaNFG4p9tYejmGJ7MqQnBd0Py2e/YARy3f89WHTN4weJluQ3mdN6gjU1ihdo4aGNnQ/kxLJ3Iw9ISXn75ZS699FJ/3ssnMXPmTO68804A1q1b1+Ttsba2lquuuooNGzaQnZ3NsGHDeOqppzjvvPNSbV5KMLIcT1K8IUNiBDIMjY5pitjy9mP0Neqpt8L0ue5VQjnuW9WZt5Ko2k6vgj1XsBUVFRQUNL6J7Zwb5eCDD8Y0TS6//HIuvvhiTjvtNP7v//6PZ599lqysLB577DFmzJjBq6++SkODk813zZo1vufxvPPO4+KLL2bp0qV+8q66zd+x44np9G/4nD6AtSKOmjaBhkgOm3Pr2XjIGaAUVu4cNh70T9g/LOSdDz8gbmX53Z49XnvtNW6//fa9F5TvYckgwQKOSMkQoZJy/DFtItQv3sLwLfH02rMbvv5+B998uJ6BX1exX7zp73TFPlmcdPXhDGvji4bhPtTCAQkWv8dKQKGE3qCNb895BGXb7Ru0sZPhNwkFFCu0O9otWBYsWNBkfvr06UyfPr1V+/z2t79NeffojiTkZrOMx0Sw7ArdzVOupzoPy2ePA7Ctx9EMyGnqAg7vRawAFBcXN+n+ntyE+fjjj3PyySfzpz819Wrcc889nHfeedTX16PrOg888AAPPPAA4NzrM2bMYMSIERx33HH867/+Ky+88AJDhzrdoAG2vfw7BjZ83minZlFa8SGDi0PcVnMgJafezOLFizn8+I30P/cu/vdfL+LFL00evaBpAPny5cs54IADmnh2dkumCpZOzPqNX6KePpeCeAUAStOcpIS4WsX+VwC+eqec4bVNH/jfnryPP20v+ZEDtsT4qneEfQiGmvo4iz5aj7HsR/bfnsBL9bk9S2NzvxwO/s4Z3dvKD7fLKxr2PCxBJc/zBUtwsVqpGrSxs+E5lTpV0K3gEI44P0grLoJlV0SU430IRfNTdsyadV/Qx/wWNMg78VdtOsaQIUNYtWoV8XicxYsX+1ma58+fz4svvshLL73kb1tVVUVBQQGWZbFlyxaeeOKJZsdLXjZ58mQmT57cbButvgKALezDhc+sZ1WZzertDVwxJsz1117NhAkTyMrK4sknnwTg1v9+jSLN5twnv6dw0U94/fXXgVY0BwF4o2Vr6cm62hXZsOAhxtWt2+36bVo5KCipbV6hTzhuP396vq3gjY0dPlafbdt88fU2Nn68kcHf1XCgG25navB1/yzyxpYy9rD+FGyshEe/cFa28+055AqWUEBBt76HJeDY8lQM2tjZ6JRBt4JDJGoQB6yEdGveFVHbeWCGs/NSdszyV+8hT4Ot9KX3wRNavN/O3ZdvuOGGZiLhyiuvJC8vj8mTJ5Odnc3rr7/Ogw8+yOuvv45SigsuuIBBgwY1Oa6ybbZ9/DyJH7/zk9JpuMnpsNE1JytkVtXXAMR7HsS7X6+kZsNX5P2X433ZoK3iuF+eCOEs9H+8SH3WWSz4t58yYOPzbAodSL9/fd0/3+9+97uWF5aXml8XwZIKbNsiv9z5Hj8efin9xv1TY08RbLAV1eU/EqvMJZHVC3c0CIyQztjDmsY32K4o8LwRqaa8sp4lH64j9/PtDKyy8DKxlOXqbD+kB6OOGciJvZK8dEnNN6qdgiXsVoeRoMZT8sIKMqQzXFfGdhP0GZ29Sag7EskKiWDZA1k4giWakxoPi23GKS57BwxIHNy6MXZee+21Zst2HqfHCwxPZubMmcycOXO3x932yYuUvPOLFtuhIk5ZaElp9fZZ82iTbbZ/+u9QOh6AfubXbH7zP+l13MWEWiv8/NT8IljaS2XVNrbPPpERNU6wdXTQUQwaOLL5hvs1X7RLXKFjpzAg3bJslq3YzLZPyjhgXR3D3WoppsNXA6OUjhvAmEP6NOt1BqBHkgVL+2wK256HJaAYFiPYGJbujN8kFJD3bHeIYGkDWVGDGkCJYNkl2SoGWuoEy9Z35lBq1NNghekz7ca97xAA8Y0rAWiwwlQavVEkxTQoJ+zYtsFWClPPocekawDI3Wco6/ufg7HlczTbRFcJImY1xUYFIRUn65Bp8O4rAJQuvAXr41vZesIj9D7uZy03LuE2CYmHpd1U1ZQz2BUrAIkNS9nSdyh9Bo5u0/E017PSszLBW39aDGj+gMONG3mfbvi61nS5csWO3jsHZdr0WrmD0jqbUnez7wo0XilexvtZL9Jg7GBmzkzG6Gft0p7sqPPyBe13VPgelhTnmNkd6WoS6pbY7j0nTUKdj7DbS0jrILduZ8ZMxIlqTnfvrFR5WBY7wbaGZlE3azheDdXYG0mxo+QYBl73QkrO1xJUzBmraEdkX/resaRV+w64/L+azG/835kUr3qIBqOQPsdewMaFc+hf5xzT0BSa3srMoaabv0iXn3d7GdDvQJb3PppRWz8G4KgVj8GKx/iu8EB2HHQeBxz5MwqKSvdylEay85zEckVxRdH6hvYZ922tP+l1Ry4ak8e1y36K7Q+DAHctvIuj+x1NaW5zO/OyIpS704lWBK++8+Ya4mW1aEo5Hg5bcYi7eySgwEzNkCahoPB77adpNHIPqdHaQCTqfGl6gNHpnYW62mq8/jtZuakRLIbl9HoJ6zZhana5TX7526y/fyqR2o3kmtvQUMTPforiQ/eSFbaNqIZqAGyj/dmW7fIfACi2N1N+xyD6GzsAqLOi1Ex5iN7HtLJ7vxd0m4Y08V2RQ6/8f2zcuJINqxegvn2XwzZ/wODKr2Hh3SQ++T3L+h4Hoy7k4NGnEQnvOafL2NH9WGTa1FXGQCU9a1XjjFKOr85xw7vLjTfRo3/GqvsjWnwwBy3eDsA3vcKow3pzxLgBDMsOs6hsUaNYcbGVzfrq9bsULJ9/XobXHy3ewvrsmx92MGz+5l2ui2tQlBXQeEquMNKkGu5wlC0xLJ2WRsGSZkMykFid8yC3lEY02sZxWXai+IYF/LjCzRar6U5Eo6ahaTqxDSvov/JBAAbUfOJs474E7Fjx5h4Fy6JFi/j1r38NQFlZGSeffDIPPvgg69at44ADDmDJkiWMGDGCWCzG1VdczjfffUdBfh4vPP0kNDh5VuxQ+6/R6DscVfZ3IrpFDxyxsj77cPpc+Sy9C0v2svcuEA9LStF0nf4DDqH/gENg8rVs2rGZrz99mj5fPs/wyi8ZvWk+bJrPugV30/eXSwiHd5+eP2ToHH3UwFad/4d1c/jmm/8AIK/n/Yw/egEbjq7Gsm0m9m+a2XZgwUB0TW8iWnRNZ0D+rsdZq/m0zJ9WBS0TGls31zAQ+DFLZ/OIIqdpRtfQNI2S/QoZnBvM4IeaBN0GiCdYxMPS6YhEnWITwdKcBlew1JFFforGEooU9abk2At2uc42T2XD2g/R45WYBfui9xjEPt/MAUDL3nOa8iOOOMLPCXTZZZdxxhlnAHDvvfcyfvx4f7s7f3oYp+Wv5bRJboX+0AH+WynhvaTJbwH9Tr+Z2jGnU7/hSxIVG4n02o8Bh09r+wEr3O63O75vt21Cc/oVl9LvpF+hpt7I198vZ+viv3HMqv9mYN06tlSW0afXvik9XyJRmTTtCNp9+u7ae1maW8rMcTO5a+Fd2MpG13Rmjpu5S+8KwKBThlD2t1VsLwxz4oSWje1Uu90J6tZQ/OTsg1tzKSlFmoSCQ3m9hCTotvMRdWNYDGkSakbcFSwNWhapy8Kye/RQhH1+/Y4/X7vhK3AFS/FR57Jx7u+wq7egGREwImjhKBgR9FAULRxFC2dhEeKj+W/x6J8e5puvVlK7/BV6NiTY/LdrKZl0LotXrcYsNXhgYZwLRoS54nDnDdK0dUIHnpCS68jdZxi5+6QoVXt12d63EdqNpmkcuN9oDtxvNPadf0FHYXRAoHM43Ci8o9G9e9zOOuAsju53NOur1zMgf8BuxQrAAYOK4Y7xu12/K8wqJ0y3V0N66z9vhGatWdSykHJEsHResrIaPSw/+eunrKipR9NgRF42YV13nGeaU6FpuGPQJU1rmuasx93G/b15WSZVk8Ztd1K5y5Om4wmb+piFZdtENZ1+JTn+MfxjkmSHd/6kzrXedrprtP/T947jz9N0Psne5F32++FTrgJK3KaNINn44t3w9VvkAnHboOqLefT/xx/2ut9b35pM6pEgNKuU+1+t585joty5IEZp9WL6LFzK+irFVWMNZk2OctzL/Zj64N/p37cPmhGhbwpzzaSMaD7Eqmj6bQkdhW3b6O5vU9dSX6WGQ42CxTR3HcO1M6W5pXsUKu2h97CesHwHNWl+ejhNQrZ4WALA6wGZygGO24IIljYQznWKLazgyy+3+YX4JbW73ykAVn29Pa3nB7g9tMi/qzbfdzC2ZjhdfjUd2/1X6ChN89fZWvK8M600A+XGqyhNd+eTpt1joukoTSdkxThs/Zu+HZXkYP74XYtsfn5lgotGhvm23GnjG1TUtCmrKAtO2C9ESNcYum8Orz01k8NHem5/X2368xqNXU99ZQr4o7RpmrvekY7+dUJjjE7SctBQlklk02dE4lVoyqY+pzeJokFo2T0aha77ub8K0weIazrmf/w86Up2qtn3UNGr3a1sy8NB7W6nPSxXTdc3jgDeJFK12TG0JudSTT+b2bHz/jtvp5Ik307HStrORuGlYjNS1AyaTCjcesHSkXgvbHkmvP68myXX/Vr88tpVUSdP7Ga9StpOcxdrycdOui8KNsZINOjssCL846657bmkXZJqua8UJOI2Zlw516mcZfGGELadQ9tGcVRomKCZaJqNrlvohkI3FEZIQzeSb2enFJXySjPJjsbDNf3pKecMShWgaRCKiGDpdBQXOYGWITRGjurDkuo6NGB4ThZhTUM1uRF2uimUauwQ4E47k870zvmkduXF8CYiIZ2sqEFFVYyYaTcei8ZjJs8ne2mSOyAkb9uM3dQvyZV/8vG/qtsf3JjP0toNuz5mB/PAwEv4OOtgHtJ2n0rdw7QVCzdYPHZqFi99ZbLyR5uTnqplxVabb8pt5v88h/EDQizfbDFhUIgtq1Zw0qA1DF6RAeP07OXyFhccwvjylwIxRYCEZoDdtvtic+1m1lWtY2DBwGaekUi4cZwspSynF1GKR0JvK4csCd6T6rEtYfBRgwVkQVkKRtxOI+37Op3HuFJgWc5/qtE0UMqmuF/rRvBONSJY2kBBVmOxPTHtEHrkt79ra1fh29qjGP3BcZTGtnHjwF4YKDRlg7LRkv7xl1nN1illU9PQQEMijvcWbNgW+WEdXdP9fVAKbMtNiW/TUFXJJ6o/D/efRG5DLX3Gn8P6+gpU7XaUsh1RZtuuUHQ+P1i9lVH7l7E25yBGjlI8fqhiSSibB1/6kglTR/Lx0H4c27+WXz/6NnULExwxajgbjhrLhqS3cO9TU8oXosnrNO9Vyt/O+7Tx3iT9aaWc8vD29aaVIlE4gFCfEaDpJCo3YJSvxUhUN/sONjYkqAjnU9HQg1Hxnbx+zSrGNtSUu9llz0dqxU6q0SulmmzQkumm847XStvFdnub3pVhWrMni0IjoeC5gfuxPH8of2xDN/e5a+Y2C5I964DGRG+FhYex777/Qn39D/TsMSHtYmXYAT15Z1wJ1o6d8sjsof3Y8Tp67/Xep+Yv33n7Jntqu95/y+Y6WF2LZSQo7VefiktLomPamSJZISI5YQxD98MGivvk02dwKZHs3feu2t13bpkWDTUN1NfUE6uNEauLE6uLEW8wSTSYWKYFaE1DEbwQheRQBV3zz5O83pl39uu7fyklA3qnukhahQiWNpAbNfwfTU2DKYIliYRSlEV7UxbtzQmHjtxlOvC98dKyz/mXhIXaKcX30Z/NY+5Nv9zjvrnffMfD66tQaIQLejLgn/68x+33Ay7ZadnvXnuX78f2ZPuODUw46xQAzryytVeRPmY//RKv9BvEjVvXkXvdQ+k2p8tTUVnFXUud5scHW5nldXPtZl+sgJMzZedEb5qmM2T/Gak1uh0Yhs7U01MUIN4Onnn3O2pX11LVJ4/rbp+abnOEABDB0gZ0Xcc0IGxBbcxMtzkZhZ3UVNQWsQLw/A9lqEKnsjYsE1vTULrBP4Yfzrl/f90ZWA4nAkTXnJZfHSdwuMpSUNR3t+9Hf/tkMQt/LPfnvTBjTYPSrAgzjhuPspyHx2dahIff/8jfLjcc4pzRI8nLymz3c8y9+GiaI/q7C9XxhD8dR6M1Hd3XVa1rVaI3oRHL9IYQzozmMaHjEcHSRsyQRthS1MU6oMGwE5MK+bbVrYdusCu5edKxPL5wEbfFDWpyC3jfz6O7ZyJWc0tq6uu4uUbDzuuz2/0e+Wgl5DldRzcU9eZ3yV+vBcvefZ//mHZii68lHSTcCjwsvYQCYUddY3NEXrR1SdNam+hNaMT0BEtAQwEI6UcESxuxDKc1tSEuHpZkWjMeye6IuP6RBstG03UuHDuGqo8/pay+ARs3uEwpbKW8YUywaTp96oC+zY6bE4m2O4339kTmC1RTczxbYenvGQhaXWOcUKiVXsXWJnoTGjG9sdxEsHQbRLC0EeW+xcZlxOYmWCl4SPZxh7n/q5YLb833l2e5mS2b9YbyPpN6RS3aso1P33i3yQY2CiurV6tsKairoSqnMdfKtSOGtmr/dGC6D01DBEsgVNXUARp9K8r3uu2uaE2iN6ERr0lIE8HSbRDB0kZsNzxABEtTrBR4WCaVlvBaLdSHIjxKx45LUlRbyaqTj8W0beKmScKyOHLBUqrckaY9sZIVq+c/+xdw1JCWpS9PJ56HJSR6JRAq6+qBHPIS8TYfoyMTvXVVLFM8LN0NESxtRIV0wCIWz/wmgiAxU/CQPH/MKD5+7R3WeW9QScdMzuCbzM49I3e3/qOipk1Fpm6g6zoRXScScn4OQ2I1LM3JZ1JlGZcOGciQXj0ZWNILo5OMfmy5hRQSD0sgVDXEIJJDnpnY+8ZCyrBMGwPQjQzIiSQEQueogTMQFXYeCvUNEsOSjLnbrKYtJ2QY/PnUjummOOzV96nILeDgHZsp1BTTSns0WT936ecsLXZEzejCXKaMSN/gbm0l7o5nE0nBdyHsncpYHCKQZ4u3NUhsyxEs0iTUfRDB0lbcHCENDeJhScZSXrK0zMR2vQ8rix33+8cNcPu8pU7St50GruuV4d2Xd4fyvVAiWILA69acjwiWIEk0WIQBIywelu6CfNNtJeIUXUx6CTXBzPCH5EHxXYzFounNxMoViXLOH3NoQFalFm8UcStD0rd3dapM56UlX4o7UGLlzhgghSXZabZECArxsLQR3RUsCcnD0gQrw18y554+lR+rq7GVwrJtbFuhlMJSCkvZ2JbNfr1L/HiWzkjIzelhZayfq2tR7d70+R0w8KGwe5Qb4xaNdt7fqtA65JtuI7o7amVcmoSa4HVrztRHpa7r9Cks3PuGnRhdiYclSKpdp2KBZBYOFM3NwxIKy33eXZBXgjbiDbNtSi+hJlgS6Jl2DNfDYopgCYRqV54XtHIcIaGduE2fIekl1G2Qb7qNeILFimd4G0jAeGlp5FGZPvwYljTb0V2oduOfClqZll9oJ66HJSxBt90G+abbSCTLqaRsiWFpQioy3Qrtw/Ow2CIbA6HGzc9TmN05e5V1VjS36g2H5DHWXZBvuo1Eoq5gkUy3TfBGa5bWiPQhMSzBEjOcuiArEk6zJd0LzfUkimDpPsg33UaiUS83vwiWZFIx+KHQPjzB0tXuzOrt21j3xT+o3r4t3aY0QfPKW+79QPGCbkWwdB8kSqyNZGWFqILGoA0BaIyb0KQ5Im3obrOc3YU8LCvmvcXbsx9GKYWmaUz5xbUcMunEdJsFgOENuikB54Giex4W6Z3VbRBp2kay3RgWTQRLE7yXzK7zqOx86DuNZt3Zqd6+zRcr4AiDt+c8kjGeFs+jZYqHJVA0t+qNStBtt0G+6TaSk+W0V+uJzKmkLMvioosuYuLEiUyfPp1EwkkZvm7dOqLRKF988QUA8+bN46ijjuLYY49lwYIFqbVB3jLTjm47fi5L6xo/7x1lm5p5L5RtU7F5U5osaornYbGVvLwEiSFNQt0O+abbSG6205pmpGJ44hQxd+5cBg8ezIIFCzjooIOYO3cuAPfeey/jx4/3t7vtttt44403eOONN5g5c2ZKbTAzfCyh1lBdXc2RRx5JXl6eL/aee+45xo0bx6RJk1i/fj0A11xzDccccwxHHXUUb775JgAbNmzghBNOYMKECfzxj38M1G7LfdM3ukhekOK+/dB2at7SdJ2i0n5psqgpXhOcKYMfBoouHpZuh3zTbSTPFSyhDPKwfPfdd4waNQqAww47jA8++IC1a9eiaRoDBw70t7Msi6KiInJzc7Esi23bUuda9zPddgHFkp2dzauvvsrZZ58NQCKR4IEHHuC9997j7rvv5u677wbgxhtv5MMPP+T111/njjvuAGDWrFncdtttvPfee7z33nts3LgxMLsrwk4+kOJQ52zb39QQ58Md1WxqiAOQ37MXU35xLZqb+l7TdaZcfg35PXul00wfP8g5c6qCboEXw5IlMSzdBhEsbSTfbRKKmAo7Q96shg8fzrx58wB45513qKio4N5772XGjBlNtotEIqxbt44tW7bwxRdfUFFRkTIbrC4UwxIKhSgpKfHn16xZw8EHH0wkEmH8+PGsWLECgMGDBwMQjUbR3YdqsngcOXIkCxcuDMzuykgUgB6RzpfI7JlN2zl84SrOXv4thy9cxTObtgNwyKQTufyRv3Dub37P5Y/8JWMCbqGxSUiaQ4PDtm1CbrUbEQ9Lt0G+6TaS73pYNKA2Q5LHnXLKKUQiEY4//nhqa2upra0FYNCgQU22e+ihh5g+fTpXX301hxxyCH369EmZDVYXfs2sqKigoKDAn7espt/77bffznXXXQc0ikfTNFmwYEFKReHe2BFyxHTPrM6VF2RTQ5wZq9f73bFt4KbV65t4WgYcPDJjPCseft6bDHlxyRgqN8La953PFJOwGuuZrE7qSRRajwiWNpKbNEJoXdxMoyWN6LrOgw8+yPz58+nZsyejR49m5cqVnHTSSbz99ttceeWVJBIJxo4dy7x583j00Ufp378/+fn5KbPBq7K7Yrfm4uJiqqqq/HnDaKwoH3/8ceLxOBdeeCEAt956K88++ywnn3wyAwYMoLS0NBAbzXicyjxHVBXEGgI5Z6r4rj7WLHeMBaytj6XDnBZjIE1CzVj6JDw0Av56qvO59MmUHr4+0fiyEI2IYOkudI2ovDQQCRvYmtOFtD6WGW9Wmzdv5vzzzycUCjF58mRuueUWfvOb3wAwffp0ZsyYQTgc5p577uHNN98kNzeXhx9+OKU2WF040+2QIUNYtWoV8XicxYsXM3LkSADmz5/Piy++yEsvveRvW1JSwosvvohlWZx33nlMnDgxEBvXfL+ORDhMOBFn0MjhgZwzVQzOjqLTNOGdAeyXHU2TRS2jLuzYl21nxotL2qncCK9cD16vKWXDKzfA/idAYf+UnCKWJFiypJdQt0EESzswDYiYUJ8hHpbS0tLddlN+4okn/OlbbrmFW265pUNs6Eq9hABOPvlkli9fzurVq7niiiu44YYbmDBhAllZWTz5pPPWeOWVV5KXl8fkyZPJzs7m9ddf54033uDee+9F13V+/etfk5eXF4i9/yj7EcjmgG1bye55RCDnTBX9siLcP3QAN61ej4UjVu4bOoB+WZkdi7PDjRnq2QGDHyY2byb+/Q9EBu1LOCAvXbsp/7ZRrHgoC8q/S5lgqal36lxLg5DRVWobYW+IYGkHlqGBqaiPZ0YMSybQ1RLHvfbaa82WnX/++U3mV69e3Wybk046iZNOOqnD7Nod/6isgfxshtdWBn7uVHBhv55M7JHP2voY+2VHM16sAOzIzgGgT3FRSo9b8cILlP1mJtg26Dp9/+0uitweaxlNj/1B05uKFs2AHoNTdoodtU5cUzyi+YHuQtdHvul2YLvKPiaCxaexW3NXkSydixX1TrLAQ3I778jB/bIijC/O7xRipTZh0uB6WHqX9EzZcRObNzeKFQDbpuw3M0ls3pyyc3QYhf3h1H93RAo4n6c+lDLvCkBVjXOfm9JDqFshHpZ2YLmCpUEEi09X6tbcGVmd6wTcjh4yKL2GdBO2bt8BQCQep7BX6gRL/PsfGsWKh20T/2Fd52gaOuwSJ2al/DvHs5JCsQJQU+cIFisigqU7IYKlHSjxsDTD6mIxLJ0J27apzHViZQYO2CfN1nQPtm538sQU1dWgR1MXHBwZtC/oelPRoutE9h24+50yjcL+KRcqHp5gUVERLN0J+bbbgQp5giUzegllAl5J6KJYAic5L0y4i6Tlz3R+rHC6uRc31Kf0uOHSUvr+212OaAE/hqVTeFcCoN4VLIhg6VZIrdYOPA9LPCEeFg/xsKQPM0mwhMKdK2lcZ2VrdS1EI/Qw4yk/dtHZZ5N7zDHEf1hHZN+BIlaSaHB7CWlZkoOlOyGCpT24Hpa4NAn5NAoWkSxBYyUau9eHpOdEIGyrq4doMT06aKTmcGmpCJVdEKs3iQKhLHmEdSekVmsPbsIi8bA04ndrFr0SOAmzUbB0lZGaM51tcadpoqfkAgmURL1T54bFw9KtEMHSHlwPSyIhMSwenodFbqzgMZMESzgkgiUItrvd4nqJQAwUK+bc65FsKffuhDxX2oHmelhMCbr1aRxLSAia5MH3JJlWMJS7d3rP7M6b96YzYjc4HpYsESzdCqnV2oHuCRbxsPhIDEv6SLhNk4ZlimAJiHq3nPM7IC2/sAfc8duyc0SwdCekVmsHutskZIlg8ZFuzenDshw3uWHJ/RgUlivMdbnhg8V0XoyiYYlh6U6IYGkHupsW2jLlAeFhS7fmtJFwuzXrHdRjZW+UVdbz8bfbKKtMbU6STMbSnDogpMuDM1Dc6H5DRmruVog/rR2EPMEiHhYfPzW/dBMKHC/oNrRzSvcAeG7xOm6duwJbOd61WWcdwnljO1FW1jZiu7d5SDwsgaK5FU1EBEu3Qr7tdmC4gsUWD4uP3ySUViu6J17QrRGwYCmrrPfFCjgvv7fN/aJbeFo8D4shHpZA0dxb3AiJUOxOyHOlHYTc9lMlHhYfv0lI6pHAMc30CJa122p9seJhKcX32+oCtSMdWO6NboiHJVA02/OwiFDsTohgaQch8bA0w3twyY0VPKYXdBtwDMt+vXKbBVkbmsagXjmB2pEOPMESMuSODxLdrWhC0iTUrZBvux2EXcHiRawLYOMljpM3zqAx3d5BesAelr6F2cw66xAMz9ugafz+rBH0LcwO1I50YPseFqlKg8RrEoqGpdy7ExJ02w58dS+CxccLuhUPefDYXgyLCv5+PG/sQI47sITvt9UxqFdOtxArAKYbwxI2pGkiSHwPi3i2uhUiWNqBxGk0R7o1pw+nW7MReAyLR9/C7G4jVDw8D4sk6gsW3R2+LSpNQt0K+bbbgf8iK09nn8bEcVIoQeM1CQUdw9KdsVyhIm/6waLc4i6viafXECFQ5FfWDmwRLM2wpUkobVi289oZSkOTUHfF9ro1S5NQoNT3dzx5yz/fkmZLhCARwdIOlDwYmuEF3cpYQsGT8IJu5b4MjEYPiwiWIAkXOmM3yThu3Yt2CZZZs2ahaRo33HCDv2zu3LlMnTqVXr16oWkay5cv3+tx5syZw7HHHktxcTHFxcVMnjyZRYsWtce0QGhsEpKHs4cl3ZrTRjTkhKTF5eEZGJb0EkoLyhPn0hTXrWjzt7148WJmz57NyJEjmyyvra1l/Pjx3HPPPS0+1oIFC7jggguYP38+CxcuZODAgZx44ols3LixreYFgu9hEb3is7zaSRb2j5qun+U00+hVWABARbR7Bb6mE9vzsEgCs0CxLW8sIal8uxNt6iVUU1PDRRddxJw5c/jtb3/bZN3FF18MwPfff9/i4z399NNN5ufMmcMLL7zAu+++yyWXXNIWEwPBc0dWVMU4+3+XoGsamuaMo6PjfDrzjqbRNM3ZBmeZv33yOs2J//CPgYam4x9P1zRnPaDpWtJy/GPru1lu6N75GpfrWqMtetJyb5mheTa40+4yHeet0rPf2bZp5bGy/BtnX7zrdv5wrwPc60BrLKvGJU3KkaRjgOY6tZLK08/8oqHrSedy50FH14wkOxz88vfP03npVVwIPzZQlZOLZVkSVxEAXpOQpOYPFuUJFvGwdCvaJFiuvvpqpk2bxuTJk5sJllRQV1dHIpGgR48eu90mFosRi8X8+aqqqpTbsTe2bayhEKipjfPZ0s2Bnz8TMQ4qwhqQC8AJn9ek2Zq2o7n9nTQUGipp+a7mAfdzV+sal+1+m72tc+ZB03axjOSYlRJsXWflLa9QlGg8b1P2Nr+rRbuKi1F0jHtxD8dsIip3td2ulilI+g6cttykZU3mVdJlOUIXzZO0zW04dt9+HLixnue0SjRt7Z4uatdFuBu01m2eOlLxdbbgGC27tt0fqKjSzeosHpZuRasFy7PPPsvSpUtZvHhxR9gDwC233EL//v2ZPHnybreZNWsWd911V4fZ0BJy6p1eGVa2wZBhBU51pxRKuZ/gTgO4y5O3cStHf5mz2S7WJc37dWzS9u6xSVqHvx/+TmoX670J71iNyxvPzy6Owc77usv0zfVYJVkYYZuIYfqbJj+WVVJF1GxeeY9fDaWl7+1Jua2lGRe+2gKDslQd4Twdvb644+3pxiilOGp1TFqE00j/fnnpNkEIkFYJlvXr13P99dfz1ltvkZWV1SEG/eEPf+B//ud/WLBgwR7Pceutt3LjjTf681VVVQwYMKBDbNodZq1JGBgxsJA50w8P9NyZypX//d+88V6Mfx67gzt++rOUHNMReDYKha0UrhT0RZu3TKmmy71lzryNrSxsZaOwQNnYysZ5gzaa+De87ZseM+k8Tc7vrfO2t/1tfFHpbeMqzqQj+ILU2YpGker9e2KXxk/bE6j+p5P/xlY2a9b8G33ZRM+RvyMSDjd6D5JOopRqXS+u9qi2tj7NW7uf55JI/nRRKqlQoTFZkCfI7eQvIGl//xiq0amSFLcWq1NoC53ZA/95KHo70sQH1RzZ0p6NrfnKW9oprTW9Kluyaa+iLMbsv3svvND1aJVgWbJkCVu3bmXMmDH+MsuyeP/993nkkUeIxWLtaje///77+f3vf88777zTLJh3Z6LRKNFotM3nSgWm62HJK0yvHZmENw5kKtuWnfgW576SSIHdY8UbMNZ8CUDhCWOJFvRKs0Vdmx3fVsDCpejAlLH9022OIHR5WiVYTjjhBFasWNFk2aWXXsqwYcO4+eab2yVW7rvvPn7729/y5ptvcvjhncRb0eAIlqKijvE2dUbc3oaEJXNc4Fjxxp5ZRjQ3jZZ0DxqqEwDI+HuCEAytEiz5+fmMGDGiybLc3Fx69uzpLy8vL2fdunVs2rQJgNWrVwNQWlpKaWkpAJdccgn9+/dn1qxZgNMMdMcdd/DMM88waNAgNm92Aljz8vLIy8vcNkrDDWrs0VO6kXpY3qBkkpcicMy406UcpaGHxevX0Zj1jmAxRJwLQiCk/Kny8ssvM3r0aKZNmwbA+eefz+jRo3n00Uf9bdatW0dZWZk//+c//5l4PM7ZZ59N3759/f/7778/1ealDKUUUbdrXZ9eOWm2JnPwEsdJd8PgsWMNAGh2WAbjCwAz5nhYRbAIQjC0e7TmBQsWNJmfPn0606dPb9U+rcnZkilUVsUw3Ki8vn1EsHh4MSwhqcQDx0o4TUK6HUmzJd0DESyCECzyGtZG/vHtDgDiKIryxf3uYdlO5S1NQsHjCRZNhdNsSffAcgWLLoJFEAJBnipt5MsfKgBo0FSnz5CaSqwO6CUktAzLbGwSEjoeM+7c7IYhv39BCAJ5qrSRolznoSCjEjfFS2kRklTlgWObTuZnHREsQWAmpElIEIJEBEsb6Vvk9AyS7rtN8fOwSLkEjm3FAWkSCgrLdNS53OuCEAwiWNpIOOQWXcblbk8ftm2zocrJ/xENtTueO+1UV1dz5JFHkpeXxxdffAHAc889x7hx45g0aRLr168HnEDzsWPHMnHiRO677z4A/vrXv3Lsscdy1FFHcdNNNwVir+dh0cTDEghWQpqEBCFIOv9TJU2Ew06Th9aKdNNdnY3lm6iM5QMwatABabam/WRnZ/Pqq6/6giORSPDAAw/wwQcfsHjxYu6++25mz54NwOOPP94kR9GFF17Iz3/+cwBf3HT00BG2LR6WIPF7CYXkvU8QgkB+aW0k7I4SKu9WjfQuLKEwWg3AB199kWZr2k8oFKKkpMSfX7NmDQcffDCRSITx48f7WZ81TePyyy9nypQpfP755wCEw45oME2TwsJCevbs2eH2ek1CuryHBIIZdwRLSFLdCkIgyC+tjXhvVbo4WHyi4SileU621Uio6wXdVlRUUFBQ4M9blvPAuv/++1m4cCEPP/wwV1xxhb/+vvvu48ADD6Rnz57k5HR8rh7bcjKvapp4WILAcgWLIYJFEAJBfmltJOR22xUPS1MSllMueR00mnc6KS4upqqqyp/3xs7yvCfDhg0DGoXMTTfdxJo1a9i6dSuffPJJh9unvCYhGSIyEEw3hiUUkfIWhCAQ33Eb8QLtDDSUyoxcLJZlcckll7Bx40YGDRrEnDlzOOigg+jf3xlJ9vbbb2fKlClcddVVrFq1CoBPP/2UjRs30qNHaoZpN5VTDuEu4GFZtGgRv/71r/nqq69YsGABp512Gs888wzffPMN1dXV9OvXD4Cqqiry8vIYNmwY9fX1GIZBLBYjGo1iGAa5ubnBeFhsx8OiI5lug8DyBYu89wlCEMgvrY2Ek9zAmRJ3O3fuXAYPHsyCBQs46KCDmDt3LoWFhSxYsIAFCxYwZcoUwBm7acGCBfztb39j3LhxKRMrAJbtlEvE6Pxa+IgjjiAnJwdd16mrqyMnJ4d+/fqRSCQoKiryx8f62c9+xvDhw9m6dStnnHEG4AzoOXHiRI455hiGDh3KyJEjO9xeP+hW65xlv2jRIiZOnMjEiRMZOnQov/zlLwFn7LFoNOr31Jo3bx5HHXUUxx57bLNhPoLEcvvwG+JhEYRA6Jw1WwYQTuoZoCwFGZCL4bvvvmPUqFEAHHbYYbz00kvU1NQwYcIE+vfvzyOPPNJEnLzwwgucffbZKbWhMTV/16jEX3vtNUzTZNSoUfz+97/npZdeIhKJ0LdvX3JznS7cf//73/npT3/KzTffTF2dE8Nzxx13cMcddwRqq1JeDEvn9LAcccQRvgC57LLLfPF37733Mn78eH+72267jTfeeINwOMzJJ5/Me++9lwZrwXJH+gxJLyFBCAT5pbWRUFLqedPLlpZmhg8fzrx58wB45513qKio4KOPPuK9997jpJNO4s4772yy/dy5cznrrLNSaoPlNgmFuoCHxWPevHlMmDABXdd3WZ5PP/0055xzTtpHSPaCbjt7plvTNPnkk0849thjWbt2LZqmMXDgQH+9ZVkUFRWRm5uLZVls27YtLXZablpnQ5qEBCEQ5JfWRrw8LACmlRmC5ZRTTiESiXD88cdTW1tLaWmpHxB6zjnnsHz5cn/bDRs2EAqFKC0tTakNptsk1BViWDyef/55zjnnHIBm5WlZFs899xznn39+Ok0EGj0seifvJZQsEO+9915mzJjRZH0kEmHdunVs2bKFL774goqKirTY6XlYpJeQIARD13kNDpjk3AuJDPGw6LrOgw8+CMCdd97JpEmT/ODP999/nyFDhvjbdkRzEIBpO0IlGurcD00P0zRZuHAhjz32GPF4HKVUk/LcvHkzW7ZsYdq0aWzcuBHLshg3bhxjxowJ3FbbaxLSO3fZP//881x00UV8++23AAwaNKjJ+oceeojp06fTo0cPDjnkEPr06ZMGKxs9LKFw1xHngpDJiGBpI+GkdNxxdxC0dLN582bOP/98QqEQkydPZujQoRx99NHk5uYSjUb5y1/+4m/74osv8vzzz6fcBi/oNmx07oemx/z58znuuOPQdZ0dO3Zw8sknNynP/v3789lnnwHwxBNPUFNTkxaxAskxLJ237JMF4ksvvcTKlSs56aSTWLFiBd988w3z589n7NixzJs3j23btnHNNdeQn5+fFlv9JqGoeFgEIQhEsLSRsKFjoTDQ/HwM6aa0tLRZr4klS5bsctsPPvigQ2ywlNtLKNx5H5rJTJkyxe9d1adPn92WJzhjCqUT38PSSXsJQVOBeNZZZ/kxVtOnT2fGjBmEw2Huuece3nzzTXJzc3n44YfTZqsfdCsxLIIQCJ23Zkszhq5hAwaQsDKkX3MG4AkWXRM3edDYyk3N34mbhJIFYjJPPPGEP33LLbdwyy23BGjVrnEdLIS6ULyWIGQy8mrQRjRN8wdqTmSIhyUTUKrrBd12FpQvWKJptqR7YLsJmHQZrVkQAkEESztwU45gmpkRw5IJWMoRKoZ4WALHaxLS9c6Zh6WzYVsiWAQhSESwtAPfw5IhvYTSjWmZ/nRXiWHpTChcwWKIYAkC7zUllC0t64IQBCJY2oHyPSwSwwIQS8T86UztJVRdXc2RRx5JXl6en+r9ueeeY9y4cUyaNIn169cDcP311zNhwgTGjh3Lc889B8Bf//pXjj32WI466ihuuummtF3D7rClSSgwLNMm4f7sc/t0/DhRgiBI0G278ARLQpqEAIibcX86GsrMh2Z2djavvvqqLzgSiQQPPPAAH3zwAYsXL+buu+9m9uzZ3H///YTDYWpqajjmmGM477zzuPDCC/n5z38O4IubAQMGpPNymiAeluBoKG/wp7N6ZqfREkHoPoiHpR14fhXxsDgk3NTwAJFwZj40Q6EQJSUl/vyaNWs4+OCDiUQijB8/nhUrVgAQdpu0amtrOeigg5osM02TwsJCP+ttpmBrnmDJTLHYlWiocLyJhgYhGfxQEAJBBEs7UJrjYrEyJDV/uklYjZ6mzjKWUEVFBQUFBf68lXQNF110ESNHjmzSzfa+++7jwAMPpGfPnuTkZFZTgO9hCWWmWOxKWHHnPhGpIgjBIYKlHUgMS1MsN+jW0DpPE1lxcTFVVVX+vGE0PoKefvppvvrqK2bNmoVtO6L0pptuYs2aNWzdupVPPvkkcHv3RGOTkHhYOhrLTWWga9JDSBCCQgRLO1DSrbkJpuud0LTO43EaMmQIq1atIh6P89FHHzFy5EgAYjHH5Z+Tk0N+fj66rvvLDMMgNzc34zwsXpOQkaHxQ10Jz8Oii14RhMDoHH77TMWtrCzJdAuAZXuVeGaXx8knn8zy5ctZvXo1V1xxBTfccAMTJkwgKyuLJ598EoALL7yQ7du3k0gkuOOOOwD4wx/+wLvvvotpmkyePNkXN5mC8mJYRLB0OL6HRV75BCEwRLC0AyeGRWFKHhYAYgmnl1Aow5uEXnvttWbLzj///CbzL774YrNt7rjjDl+8ZCJKc5vkRLB0ONIkJAjBI+8H7cHzsNiZ7VEICq+XkKGLgEsHSncHPxTB0uFYMTfoVtqEBCEwRLC0B0+wiIcFgIQbdBvqRDEsXQnfwxLJSrMlXR/Ti2ERwSIIgSGCpT343ZrFwwIQt7y3ThEsQWPbNsrwgm5FsHQ0pudhkXGEBCEwRLC0B7f0TBEsQONYQpkedNsVUUlZhvWwCJaOxmxwBEsoJIJFEIJCBEt7cD0stiSOA8CyvUBEESxBY8UbU8UbEUkV39EkYp5gkSpUEIJCfm3twS09yXTr4CVXM3QRLEFjJRoFi2S67Xh8wRKWKlQQgkJ+be1BYliaYHaSPCxdEdt0R8q2DXRDEsZ3NF7QrQgWQQgO+bW1A80tPVt6CQFQU/OdM6Hq02tIN8SKOR4WzZbUSkFgxp3ffCgs4lAQgkIESzvQ3C6NkofFIRavBEBHyiNo4lsrANBtaQ4KAjPheFjCEalCBSEo5HWsPVgxQOejFWuZe+tCFKDcLqUKDaXcT3feVho2zr8CLKW70+7ypPURzSKuDP/hv2sJkNxDofUiIYyFgU1Isxs/NZsQCl1ThDTlLlcYmsLQwPCWa07Tj6ZpaDgZP+dV7ecc17Z4av6EnezTmn5qTddFsvbh1DH/QXa4gGQ++PoxNmx9C9DQNAM0HQ3d+dQMNE1Hw/3UjJ2WGehJy3Qt5C/zlut6CA13nW5geNvoIXTNwNDDaFoIQwujaZq7L+jo6JqGpukYWohQKBdDj2BoEXQ95EzrIUKGu0wLY+hhdF3HtOIkrDriZh1xq46EWUfCqvf/Tase04ph2g1YtomlFDYKDR1N0wDdn9bccjG2byARLyRevh/rF25y1uka6BqaBrqhOWWo4y5z1mu6+43oeuO87u6rOV+TpjsbOWnoNTTDPY6mNzu2s5+33vnUu2D+em/AUyMiHhZBCAoRLO0g164Eiqkjh6/UQGdhIkUHD9JJkeJzlZoV9FUbWnfOurXMfW8i4dwR5OUM4ojB/8QbS35BX/tbeqfWvCYmBD2IgK06ZsA8M17It//vHrBDfPfRV6k/QYrQdvGZrF2byFgteZnWZJm/RNvVPtpe1oOWtMJf32zbpsdJ3raqzunCH5YYFkEIDBEs7WB4bhVfVxRzQd4yrhsRRuuxH1p2YZOK0vNAGO5brfPS63xqmoahu+/L3hsvzoN0XaVNzxydvIjz9gpJFWhSTZ5cmTfW5rvyYjRdbyswbTBtG8uy3H+FaTvTpmVj2RaWZbvTO38qlG1jKxulnOnyLSvo+eNqDsj5kk35JwEKpTxl0vQzebllN1BQ+zGleiXUfwT1H/Fm5RL62t/6pm82hqLpYZSyAdv/RNnOMZWNonEabDSlnG3cZRoK5fuzFCjP99X46fyTNN24LFllKe9fOd6miKbQwfVC7f6e2Vms2ApM5QgnU2lYaL7nzUYHTUN3v9HGXZvaowHxyr5ghwCbnJDhXHJyiStQSd46r/h33qb5N5VaPbvLY+7uBC3aaK87diiF/fMCO5cgdHdEsLSDkNt9d2Df/hx+/sUpPfaQlB4tGJa+8w8O+/AdVkX6cNTYP7Vq38/Xv8wXa+dQGP+SbF1RkvjKf0L3HvoIJ/T/SQdYnFqUUtjKEXGmncCyE1h23P1PYKkEtm0SMqJEQrmEjWwiRjYhPdQoQtvImx9+yA7iVOeXc/V956boilxh6QkZ23Y0ogKlbJTlih9bga38bZWtnH/l7u9N24CysW1AOds0295b1mQ/V2p5+6nGbZSdZKNS2F48mbsOzz73WlyN6h5P+ULO2dY7rn/1jq007uceGlDkFGex74T+KStrQRD2jAiWdhCOOA8ZLydDt8dysq3abYhZOHTAaRw64DT+5+2RZFPreynKjCGdQqyA6zHTDAwMwkY40HMnEk4TBSkeFkHTkppO9OR4DYndEAQhWESwtINotlN8DfXSrRlA+YKl7Q+z4UP/jVU//BWlTDTNYNzQW1JlXpcmHk8ABhjSQ0sQhK6JCJZ2EM11upDGGvayYTdBWU7EsdLafluNGngGowaekSKLug+JhIUIFkEQujIS4t4OCvr2AGB7ZU6aLckMfMHSDg+L0DZMr0lIBIsgCF0UESztoM/okQBUxHsTK9+WZmsyAL9JSBx3QZNwU8W3w7klCIKQ0YhgaQfZffpQEHaEytZl/0izNelH2eJhSRem6QoW8bAIgtBFEcHSTvr0qAFgy1cb02xJBuA3CclrftB4qeK1UAdkpRMEQcgARLC0kz77ZgOwZaOZZksyANfDgiGCJWhsN1W8aEVBELoqIljaSZ+DnPFztlQUO4m1ujO2lwsk2BwkApgJ597TxcMiCEIXRQRLO+k1ciQ6JvV2AdVrv937Dl0YzXIEizQJBY9tOoLFCMlPWhCEronUbu0klJNDr5wtAGz5/Ms0W5NmPA+LNAkFjuUXvfykBUHomkjtlgL69HZiN7Z8uz3NlqQXTZqE0oZyY1gMGT1YEIQuitRuKaDPYCeB3JYt3bs7r2Z5QbciWILGC7oNiYdFEIQuitRuKaD3iAMA2FZbkmZL0ot4WNKHspxg21C4e4tmQRC6LiJYUkB2iSNUTBXFjifSbE360GwveVkkzZZ0P5Q7YLgIFkEQuioiWFJAKKdxLCGzvjaNlqQX3Rcs4mEJHPGwCILQxRHBkgKM7CTBUleXRkvSi9ckJIIleLwmoXBYemgJgtA1kdotBWi6TkiLYaooZn16BcuiRYv49a9/DUBZWRknn3wyy5YtA6C+vp54PM6yZcv405/+xH333cfhhx/OCy+8kJJz60qahNKF5gqWiAgWQRC6KFK7pYiQFncES119Wu044ogjWLBgAQCXXXYZZ5xxBg8++CAATz31FN9+6yS3O+ecczjppJO4+eabU3ZuvQvEsOxK8C1ZsgRd14nH48yePZsRI0YQi8W47rrrWLNmDQUFBbz00kvpNdxynKXiYREEoasiTUIpIqQ7zSFmQ0OaLXEwTZNPPvmEY4891l/2/PPPc8455wDQu3dvDCO18Q66OzRBZxYsnuBbsGABxx57LGeccQbvvvsuCxYsYNasWb74e/jhh5k2bRrz5s1Lv1gBNNsVLBERLIIgdE1EsKSIkO70DjIbYmm2xGHevHlMmDABXXe+4urqatavX89BBx3UYefsSk1CyYIvHHZicqqqqjjkkEMAeOONN/jggw+YOHEijz32WDpNBUB3PSyRiMQPCYLQNWmXYJk1axaapnHDDTf4y+bOncvUqVPp1asXmqaxfPnyvR5n5cqV/PSnP2XQoEFomsZDDz3UHrPSgqE7D2uzPjM8LMneFICXX36Z0047rUPP6XlYdCPaoecJgmTB9+OPPzJ+/HiuuuoqjjvuOADWr1/PuHHjeOedd3jmmWfYsGFDWu3VbMdbFg13frEoCIKwK9osWBYvXszs2bMZOXJkk+W1tbWMHz+ee+65p8XHqqurY/Dgwdxzzz2Ulpa21aS0EjZcwRJLfx4W0zRZuHCh/3CF5gKmI/A8LHqo8z80k8urpKSEjz76iBdffJHbbrsNgKKiIk444QRCoRBHH300X3/9dTrNRXcFS1a085e9IAjCrmiTYKmpqeGiiy5izpw5FBcXN1l38cUX85vf/IbJkye3+Hhjx47lvvvu4/zzzyca7Zxv56GQ410wG+JptgTmz5/Pcccd16Q5aN26dRx88MH+Ns8++yw/+9nP+OCDD5g8eTK26x1pD4bqGh6WZMFnmqZfNoWFheTm5gIwfvx433v4+eefM2jQoDRZ6+AJlkhEBIsgCF2TNkXoXX311UybNo3Jkyfz29/+NtU2tYhYLEYs1hgvUlVVlRY7PEKGM5aLmQGZbqdMmcKUKVP8+fz8fJYuXdpkm/PPP5/zzz8/pecNeU1Coc4tWJIFX1lZGRdeeCG6rqPrOn/6058AuPnmm5k+fTq33XYbJ510EoMHD06rzYZyfspZIlgEQeiitFqwPPvssyxdupTFixd3hD0tZtasWdx1111ptSGZUMgVLDEzzZakD0M5ZdDZPSzJgq9///689957zbbp06cPr7/+etCm7ZKEaaIrN4Yl0rnLXhAEYXe0qklo/fr1XH/99Tz11FNkZWV1lE0t4tZbb6WystL/X79+fVrtCbmdM8x4dxYsjofFCKX33uhuNMQbA72zO2mTqiAIwt5olYdlyZIlbN26lTFjxvjLLMvi/fff55FHHiEWi6U8t8fuiEajGRXv4gmWRLz9sSCdla7iYels1Cc1jWaJh0UQhC5KqwTLCSecwIoVK5osu/TSSxk2bBg333xzYGIlEwmFndToZtxKsyXpI+QKFiMsHpYgicUbA70jYcnDIghC16RVgiU/P58RI0Y0WZabm0vPnj395eXl5axbt45NmzYBsHr1agBKS0v9LsuXXHIJ/fv3Z9asWQDE43FWrVrlT2/cuJHly5eTl5fHkCFD2nF5wRGKOGLNSqg0W5I+QtIklBZiMUewmHrC7xkmCILQ1Uh57fbyyy8zevRopk2bBji9UUaPHs2jjz7qb7Nu3TrKysr8+U2bNjF69GhGjx5NWVkZ999/P6NHj+ayyy5LtXkdRijsFGUi/Z2E0obvYRHBEigNcadJyNa6b/yUIAhdn3YPPOINtOcxffp0pk+f3qp9Bg0ahFKd2zMRcsdwMbupYFFK+TeTCJZgibkq2da7b3OkIAhdH/Efp4hw1BUsppZmS9KDZcX9mykcyk6rLd2NeFwEiyAIXR8RLCkiFHWCHU2rewoWM1HnTxthESxB4sWwKKP79lATBKHrI4IlRYSynAyjptk9i9Q0G3OBhKRJKFDibpOQ0kWwCILQdemeT9cOwPew2N2za7fVRLCIhyVIRLAIgtAdEMGSIkLZjlfBtLqnYEkk6v1pw5DxbILEz/0jTUKCIHRhRLCkiFCWk2HUtLtn4i7PwxIH0LpnHE+6SCTc7sxG5+5pJwiCsCdEsKSIULbTDJLotoLF8bCYIlYCRwSLIAjdAREsKSKU4wgW0+6ezSGW5XhYRLAETyLhNQml1w5BEISORARLigjn5AJgEUGZ3S8fhmU62VYtTW6poLHc+00LiYdFEISuizxdUoSR3dgzxmyo28OWXRPbFSziYQkeM+EE2+qGlL0gCF0XESwpIuR6WADM2u4nWLygW/GwBE+jhyXNhgiCIHQg8nRJEXoohIE7am59bZqtCR5bmoTShmU6TUF6SDwsgiB0XeTpkkJCuitY6ur3smXXw3aDbi1dHppBY0mTkCAI3QARLCkkpDsZR836bihYTEesWZp0VQkaz8NiiIdFEIQujAiWFBLSnXwYZkMszZYEj215gkVuqaCxLVewhKXsBUHoukgNl0JCuhP82B0Fi7Kca7Z1uaWCRvkeFil7QRC6LlLDpZCQ4XhYEt1QsCQSTs8o6dYcPMp0ylwEiyAIXRnpCJlCQu7gc9vXbiNn4cJdbmMnTH78ZgPhaBhN19x/Hd3Q0XQNXdfQDB1Nd/51XUcznG2c5Ya7j+HMazqaYfjb+9P+MgMtZKDpBnoo5OxrGGhGCM0w0N1P75xt5R9bljAGqEx0vy7d6Ua5eQrDYfk5C4LQdZEaLoWE3Uyji77oz6Iv9hR426cNR1eA5f53FDY6Nho2mqbQsNE1Gx0LXfP+bQzdXa7b6JrC0G3yEj/hVTWJHYbivd89ha6DZnj/jhDTDVegabifGpqOI9J0DV1zPw0NNGdeNzRHtOkaxk6fuu6sc/6d5bqh+5+6pqMbGoZuYOg6hmGg6xohw0DXdUKuqAvpBobhrDcMw58PGSFnez3kLHf3yzQ8wRKSGBZBELowIlhSyPCjS6l8eyuWvecHR8zKwlYG/Yq2ohTYSkMpzf90lukoBUrpznKc5QoN3G3B3T75Ew2ldHdad5fr2OjsvQXQ2w5HHyV/toL8VjhZPBnWPmz3s+OHRFDYKE1hu59KswHlTrvzWuO8Nw3eNOAuR8dfrzlfZ9Jy519zt9E0zfnUtcblOmiahrHDSVoYEg+LIAhdGKnhUsjgU37C4FPSbcVuUApl2yjLQpkmtm2iTAtlWyjLxjYtlG06693tbKtxnWVa2KaJnXD/TRPLNLETlrs+wcbqrWzJ6UmCEKZpY1kWtmljWQrbUtiWjbKVK8RA2aCUAtuZRoGylfOpAKX5y1E4ukRp7noNzd2mcXnjMs0VdN605q/X/XkN3V+vozcu2wMauqspjDaJuY6ksCB37xsJgiB0UkSwdBc0L3bFgEikQwb2HdYBxwwa27YxLQvLsjBtE9tWmJaJZdmYtrPc+XfmbcvCsm1/P9tWznrbxrItbMuZV0o521m2u73CdvezbRtlg2U7gs77tG3bEXpKOULP/ffmbVv5oi+vMItJ405Od/EJgiB0GCJYBCEJXdeJ6DqEw+k2RRAEQUhCovQEQRAEQch4RLAIgiAIgpDxiGARBEEQBCHjEcEiCIIgCELGI4JFEARBEISMRwSLIAiCIAgZjwgWQRAEQRAyHhEsgiAIgiBkPCJYBEEQBEHIeESwCIIgCIKQ8YhgEQRBEAQh4xHBIgiCIAhCxiOCRRAEQRCEjKfLjNaslAKgqqoqzZYIgiAIgtBSvOe29xzfHV1GsFRXVwMwYMCANFsiCIIgCEJrqa6uprCwcLfrNbU3SdNJsG2bTZs2kZ+fj6Zp6TYn7VRVVTFgwADWr19PQUFBus3pdkj5pxcp//QhZZ9eOmP5K6Worq6mX79+6PruI1W6jIdF13X22WefdJuRcRQUFHSam7YrIuWfXqT804eUfXrpbOW/J8+KhwTdCoIgCIKQ8YhgEQRBEAQh4xHB0kWJRqPMnDmTaDSablO6JVL+6UXKP31I2aeXrlz+XSboVhAEQRCErot4WARBEARByHhEsAiCIAiCkPGIYBEEQRAEIeMRwSIIgiAIQsYjgqWT8vXXX3P66afTq1cvCgoKGD9+PPPnz/fXf/7551xwwQUMGDCA7Oxshg8fzr//+7/v9bhXXHEF+++/P9nZ2ZSUlHD66afz1VdfdeSldEo6ovzLy8u59tprGTp0KDk5OQwcOJDrrruOysrKjr6cTkdH3f+zZ89m4sSJFBQUoGkaFRUVHXgVnZeOKv9YLMa1115Lr169yM3N5bTTTmPDhg0deSmdjr2VPcD111/PmDFjiEajjBo1qkXH/fbbbznzzDMpKSmhoKCAc889ly1btnTAFbQdESydlGnTpmGaJvPmzWPJkiWMGjWKU045hc2bNwOwZMkSSkpKeOqpp1i5ciW33347t956K4888sgejztmzBgef/xxvvzyS958802UUpx44olYlhXEZXUaOqL8N23axKZNm7j//vtZsWIFTzzxBG+88Qb//M//HNRldRo66v6vq6vjpJNO4rbbbgviMjotHVX+N9xwA3//+9959tln+fDDD6mpqeGUU06R+ieJvZU9OKnu/+mf/onzzjuvRcesra3lxBNPRNM05s2bx0cffUQ8HufUU0/Ftu2OupTWo4ROx48//qgA9f777/vLqqqqFKDeeeed3e531VVXqeOPP75V5/r8888VoL755ps229vVCLL8//d//1dFIhGVSCTabG9XI4jynz9/vgLUjh072mtul6Ojyr+iokKFw2H17LPP+ss2btyodF1Xb7zxRmqM7+S0tuxnzpypDj300L0e980331S6rqvKykp/WXl5uQLU22+/nRLbU4F4WDohPXv2ZPjw4Tz55JPU1tZimiaPPfYYffr0YcyYMbvdr7Kykh49erT4PLW1tTz++OPst99+Mgp2EkGVv7dPQUEBoVCXGfar3QRZ/kJzOqr8lyxZQiKR4MQTT/SX9evXjxEjRvDxxx+n9Bo6K20t+70Ri8XQNK1JsrmsrCx0XefDDz9MhempId2KSWgbGzZsUGPGjFGapinDMFS/fv3UsmXLdrv9xx9/rMLhsHrrrbf2euw//elPKjc3VwFq2LBh4l3ZBR1Z/h7btm1TAwcOVLfffnsKLO5adHT5i4dlz3RE+T/99NMqEok0Wz5lyhT1i1/8IhVmdwlaU/Yt9bBs3bpVFRQUqOuvv17V1taqmpoadfXVVysgo8pePCwZxJ133ommaXv8/+yzz1BKcdVVV9G7d28++OADFi1axOmnn84pp5xCWVlZs+OuXLmS008/nd/85jdMmTJlr3ZcdNFFLFu2jPfee48DDjiAc889l4aGho645IwiU8ofnCHip02bxkEHHcTMmTNTfakZSSaVf3ckU8tfKYWmaam4xIylo8q+pZSUlPD888/zyiuvkJeXR2FhIZWVlRx22GEYhpHCK20fkpo/g9i2bRvbtm3b4zaDBg3io48+4sQTT2THjh1Nhg8/4IAD+Od//mduueUWf9mqVas4/vjjueyyy/jd737Xapvi8TjFxcX813/9FxdccEGr9+9MZEr5V1dXM3XqVHJycnj11VfJyspq2wV1MjKl/AEWLFjA8ccfz44dOygqKmr1tXRG0l3+8+bN44QTTqC8vJzi4mJ/+aGHHsoZZ5zBXXfd1cYry3w6ouzBEUIvvfQSy5cvb5UtoVCIoqIiSktL+dWvfsVNN93UquvpKKRhPIPo1asXvXr12ut2dXV1AOh6UweZrutNIrpXrlzJpEmT+PnPf94mseKhlCIWi7V5/85CJpR/VVUVU6dOJRqN8vLLL3cbsQKZUf7dmXSX/5gxYwiHw7z99tuce+65AJSVlfHFF1/whz/8oTWX0ulIddm31xZwBOTWrVs57bTTUnLclJC2xiihzfz444+qZ8+e6qyzzlLLly9Xq1evVjNmzFDhcFgtX75cKaXUF198oUpKStRFF12kysrK/P+tW7f6x9mwYYMaOnSo+vTTT5VSSn377bfq97//vfrss8/UDz/8oD7++GN1+umnqx49eqgtW7ak5VozkY4q/6qqKnXkkUeqQw45RH3zzTdN9jNNMy3Xmol0VPkrpVRZWZlatmyZmjNnjt8bY9myZWr79u2BX2em0pHlf+WVV6p99tlHvfPOO2rp0qVq0qRJ6tBDD5X736UlZa+UUmvWrFHLli1TV1xxhTrwwAPVsmXL1LJly1QsFlNK7brs//KXv6iFCxeqb775Rv3tb39TPXr0UDfeeGPg17gnRLB0UhYvXqxOPPFE1aNHD5Wfn6+OOuoo9dprr/nrZ86cqYBm//vuu6+/zdq1axWg5s+fr5RyuhD+5Cc/Ub1791bhcFjts88+6sILL1RfffVVwFeX+XRE+XuBnrv6X7t2bbAXmOF0RPnvab/HH388uIvrBHRU+dfX16trrrlG9ejRQ2VnZ6tTTjlFrVu3LsAry3z2VvZKKTVhwoQ91iO7Kvubb75Z9enTR4XDYXXAAQeoP/7xj8q27QCvbO9IDIsgCIIgCBmP9BISBEEQBCHjEcEiCIIgCELGI4JFEARBEISMRwSLIAiCIAgZjwgWQRAEQRAyHhEsgiAIgiBkPCJYBEEQBEHIeESwCIIgCIKQ8YhgEQRBEAQh4xHBIgiCIAhCxiOCRRAEQRCEjEcEiyAIgiAIGc//B9HJXXQVK2WYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"And now the Lorain - Erie - Huron cluster.  Each Lorain straddler fills with Erie or Huron partial\") #DONE 3/19\n",
    "mC = 46 #main county = Lorain\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "pairedCountyList = [21,38]  #Erie, Huron  (Ashland & Cuyahoga are tied up)\n",
    "#spcList = [ 38, 21 ]  #for both Erie and Huron, only need a partial to complete the straddler; no 2dary pc needed  \n",
    "targetRemnantPop = avgDistrictPop * (countyDistricts[mC]%1.)  #the natural pop left after filling whole districts in main county\n",
    "luringCountyList = pairedCountyList.copy()\n",
    "luringList = list()\n",
    "forcedTractList = [ ]  #no forcing tracts needed for Lorain; will find the west tracts\n",
    "\n",
    "for c in luringCountyList:\n",
    "    for tt in countyTractList[c]:\n",
    "        luringList.append(tt)   #these tracts in nearby counties \"lure\" HDs which contain them to center straddler HDs\n",
    "forcedGeom = countyGeom[mC]   #default; we do not force any tracts to be in the original HDPoly's of straddler districts\n",
    "if len(forcedTractList) > 0:\n",
    "    forcedGeom = tractCP[forcedTractList[0]]\n",
    "    for T in forcedTractList:\n",
    "        forcedGeom = forcedGeom.union(tractCP[T])\n",
    "\n",
    "countyStraddlerList[mC] = get_straddlers(targetRemnantPop, countyPop[mC], countyTractList[mC],\n",
    "                                         luringList, forcedGeom, HDPoly, tractCP, tractPop3)\n",
    "mC_EnsnaredPop = 0.\n",
    "for t in countyTractList[mC]:\n",
    "    if t in countyStraddlerList[mC]:\n",
    "        HDtype[t] = \"straddlerMaker\"        \n",
    "        plt.text(HDCPx[t],HDCPy[t],t,fontsize=6)\n",
    "        mC_EnsnaredPop += tractPop3[t]\n",
    "    else:\n",
    "        HDtype[t] = \"notAstraddlerMaker\"\n",
    "        plt.scatter(HDCPx[t],HDCPy[t],marker=\".\",label=\"not a straddler generator\")\n",
    "for m in countyMuniList[mC]:\n",
    "    plotPoly(muniGeom[m])\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyPCP[mC].buffer(0.01))\n",
    "print(\"We ID'd\",mC_EnsnaredPop,\"pop of county\",mC,\"vtds to be straddlers, vs\",int(countyPop[mC]%aDP),\"target.  See X's below\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "28aba4ea-03c6-4bd5-a2a0-b3b61670b7ea",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "if we have discontig problems w straddler districts, we could force-add the following ...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"if we have discontig problems w straddler districts, we could force-add the following ...\")\n",
    "ppCMustList = [[1122, 1120, 947,948, 1121, 1034],[771,736,957,949, 1034] ]\n",
    "for i, L in enumerate(ppCMustList):\n",
    "    for v in L:\n",
    "        plotPoly(tractGeom[v])\n",
    "    plotPoly(countyGeom[pairedCountyList[i]])\n",
    "    plotPoly(countyGeom[mC])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c9d70997-1933-45d2-a017-bbb170930065",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are your 46 county and ppC county muni's > 10K pop\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here are your\",mC,\"county and ppC county muni's > 10K pop\")\n",
    "for m in countyMuniList[mC]:\n",
    "    if muniPop[m] > 10000:\n",
    "        plotPoly(muniGeom[m])\n",
    "        plotCenter(muniPop[m],muniGeom[m])\n",
    "plotPoly(countyGeom[mC])\n",
    "for PPC in pairedCountyList:\n",
    "    for m in countyMuniList[PPC]:\n",
    "        if muniPop[m] > 10000:\n",
    "            plotPoly(muniGeom[m])\n",
    "            plotCenter(muniPop[m],muniGeom[m])\n",
    "    plotPoly(countyGeom[PPC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b940dc41-9844-4e6e-9294-41ed3351aa1a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51\n"
     ]
    }
   ],
   "source": [
    "print(len(countyStraddlerList[mC]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "02c0c8e8-a18f-431a-925c-f76516f41a7a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, we build the 3-district regional plan.  Straddler + 2-district pizza cut\n",
      "given the constraints in NE Lorain County, the straddler districts cannot be split in Lorain\n",
      "     742 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "949 2 119198.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 949\n",
      "     1034 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "957 2 115627.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 957\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "     738 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "     739 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     841 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     842 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     851 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "1115 2 115627.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 1115\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "     846 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "1053 2 119198.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 1053\n",
      "1033 2 115264.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 1033\n",
      "1025 2 121069.0 False t,i,vtd-pop,contiguity = FAIL\n",
      "1025 2 115264.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 2 of 3 legal districts for vtd-, muni-snap for vtd 1025\n",
      "     747 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     894 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "771 2 119198.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 771\n",
      "748 2 115264.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 748\n",
      "746 2 115627.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 746\n",
      "     736 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     982 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     844 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     843 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "     849 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "1026 2 115264.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 1026\n",
      "     845 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "1029 2 115264.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 1029\n",
      "     848 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     767 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "1027 2 115627.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 3 2 of 3 legal districts for vtd-, muni-snap for vtd 1027\n",
      "     749 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     847 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     1052 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "the straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\n",
      "     754 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     740 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "     1044 has drawn a GOOD straddler +2-district Lorain; add to growing list\n",
      "all done for main county 46 we generated a total of 24 good plans out of possible 51\n"
     ]
    }
   ],
   "source": [
    "print(\"now, we build the 3-district regional plan.  Straddler + 2-district pizza cut\")\n",
    "print(\"given the constraints in NE Lorain County, the straddler districts cannot be split in Lorain\")\n",
    "Lorain3LISTS = list()  #a list of 3 lists of vtds in a 3-district area vtd-snapped regional plan\n",
    "Lorain3LISTS3 = list()  #same, for muni-snapped\n",
    "Lorain3Starters = list() #the starter vtd No for the two above lists\n",
    "LorainStraddlerAngles = list()  #FYI, the cut angle for making the in-Lorain straddler\n",
    "LorainCutAngles = list()         #FYI, the cut angle for the 2-district Lorain\n",
    "badLorainStraddlerAngles = list()  #FYI, for those straddler-initiators that failed to make the cut!\n",
    "angleNoise = 0.3   #we'll add some wobble to straddler cut angles to get better sampling, hopefully\n",
    "toler = 0.05*aDP\n",
    "      \n",
    "for t in countyStraddlerList[mC]:  #obtained in above block\n",
    "    homeM = tractMuniNo[t]\n",
    "    nSplits = 0  #tracking total splits in all counties composing this straddler district.  Force to 0-1\n",
    "    HDangle = [HDangle0[t],  HDangle1[t], HDangle2[t], HDangle3[t] ]\n",
    "    HDrad4 = [4.*HDradius0[t], 4.*HDradius1[t], 4.*HDradius2[t], 4.*HDradius3[t] ]\n",
    "    targetICP = countyPop[mC]%aDP  #target in-county Pop.  reset in case we adjusted for previous tract\n",
    "    adjCountyHDPolyPop = [0.] * nCounties  #for Montgomery, we need special rules based on which adjacent counties we spill into\n",
    "    for cc in pairedCountyList:\n",
    "        for tt in countyTractList[cc]:\n",
    "            overlap = HDPoly[t].intersection(tractGeom[tt]).area\n",
    "            if overlap > 0:\n",
    "                adjCountyHDPolyPop[cc] += tractPop[tt] * overlap / tractGeom[tt].area\n",
    "    ppC = adjCountyHDPolyPop.index(np.max(adjCountyHDPolyPop) ) #the primary pairing county had the most pop in this t's orig HD\n",
    "    #spC = spcGroups[pairedCountyList.index(ppC)]  #Lorain is simple; Erie + Huron = ppC, no spC needed   \n",
    "    mCangle = getSliceAngle(tractCP[t],countyPCP[mC],tractCP[t]) + random.uniform(-1.*angleNoise,angleNoise)\n",
    "    mClist, mCsnapPop = cutVtdSnap(targetICP, toler, tractCP, countyTractList[mC],neighborList,tractPop3, mCangle, countyPCP[mC],t)\n",
    "    #tried cut HDvtdSnap and HDmuniSnap but they often left discontiguous 2-district remnants\n",
    "    mClist3, mcML3, mCsnapPop3, splitMuni = cutMuniSnap(targetICP, toler, muniTractList, tractCP, countyTractList[mC],\n",
    "                                                        muniNeighborList,tractPop3, muniPop, muniPCP, mCangle, countyPCP[mC])\n",
    "    popGap = targetICP - mCsnapPop3\n",
    "    mClist3B, mCsnapPop3B = list(),0.\n",
    "    if (abs(popGap) > toler) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "        if popGap < 0  :\n",
    "            raise Exception(\"error! mCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "        mClist3B, mCsnapPop3B = getFillList3noSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                    mcML3, countyMuniList[mC],tractCP, neighborList,tractPop3)\n",
    "        if mCsnapPop3B == 0. :\n",
    "            print(\"uh oh, failed to create whole-muni in inCounty part of straddler district for tract\",t,\"we are short by\",popGap)\n",
    "        \n",
    "        mClist3, mCsnapPop3 = mClist3 + mClist3B, mCsnapPop3 + mCsnapPop3B  #completing in the inCounty partial despite splitM flag\n",
    "    \n",
    "    snappedHDpop[t] = mCsnapPop\n",
    "    snappedHD3pop[t] = mCsnapPop3\n",
    "    HDtractList[t]  = mClist.copy()\n",
    "    HDtractList3[t] = mClist3.copy()\n",
    "    #print(\"t,mCsnapPop, mCsnapPop3 are\",t,mCsnapPop, mCsnapPop3,\"vs tgt ICP\",targetICP)\n",
    "    isFullppC, isFullppC3 = False, False  #see Dayton or Toledo for more complicated pairing scenarios, adding full ppC + part spC\n",
    "    ppCcT = getClosestTract(tractCP[t],countyTractList[ppC],tractCP)\n",
    "    ppCangle = getSliceAngle(tractCP[ppCcT],countyPCP[ppC],tractCP[ppCcT])\n",
    "    if not isFullppC:\n",
    "        ppCtgtPop = aDP - mCsnapPop\n",
    "        ppClist, ppCsnapPop = cutVtdSnap(ppCtgtPop, toler, tractCP, countyTractList[ppC],neighborList,tractPop3,\n",
    "                                        ppCangle, countyPCP[ppC]) \n",
    "    if not isFullppC3:\n",
    "        ppCtgtPop3 = aDP - mCsnapPop3\n",
    "        ppClist3, ppcML3, ppCsnapPop3, splitMuni = cutMuniSnap(ppCtgtPop3, toler, muniTractList, tractCP, countyTractList[ppC],\n",
    "                                                             muniNeighborList,tractPop3, muniPop, muniPCP, ppCangle, countyPCP[ppC])  \n",
    "        popGap = ppCtgtPop3 - ppCsnapPop3\n",
    "        #print(t,int(ppCtgtPop3), ppCsnapPop3, \"t,tgt ppC3, actual\",ppcML3,\"=ppcML3\")\n",
    "        if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "            if popGap < 0  :\n",
    "                print(\"munisnap pop of\",mCsnapPop3,\"in\",mC,\"and\",ppCsnapPop3,\"in cty\",ppC,\"vs tgt\",ppCtgtPop3)\n",
    "                raise Exception(\"error! ppCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            # 3/4/23 NOTE:  WE COULD TRY A WORKAROUND IF ppcML3 is blank.  Requires us to ID all ppC muni's contiguous to mC HDlist\n",
    "            ppClist3B, ppCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,ppcML3,\n",
    "                                                        countyMuniList[ppC],tractCP, neighborList,tractPop3,0)        \n",
    "            ppClist3, ppCsnapPop3 = ppClist3 + ppClist3B, ppCsnapPop3 + ppCsnapPop3B\n",
    "        #print(t,ppCsnapPop, ppCsnapPop3,\"ppC pops vs tgts\",int(ppCtgtPop), int(ppCtgtPop3))\n",
    "    \n",
    "    HDtractList[t]  += ppClist\n",
    "    HDtractList3[t] += ppClist3\n",
    "    snappedHDpop[t]  += ppCsnapPop\n",
    "    snappedHD3pop[t] += ppCsnapPop3\n",
    "    LISTS = [  HDtractList[t] ]\n",
    "    LISTS3 = [ HDtractList3[t] ]\n",
    "    LISTpops =  [snappedHDpop[t] ]\n",
    "    LISTpops3 = [snappedHD3pop[t] ]\n",
    "    #print(\"after adding ppC, t, HDpop and pop3 are\",t, snappedHDpop[t], snappedHD3pop[t])\n",
    "    # deleted all spC code here -- not needed for Lorain\n",
    "    doubleList, doubleList3, doublePop, doublePop3 = list(), list(), 0., 0.\n",
    "    for v in countyTractList[mC]:\n",
    "        if v not in LISTS[0]:\n",
    "            doubleList.append(v)\n",
    "            doublePop  += tractPop3[v]\n",
    "    muniPool3 = list()  #the list of munis in remaining 2-district set (we did not allow splitMuni in prev cut)\n",
    "    for v in countyTractList[mC]:\n",
    "        if v not in LISTS3[0]:\n",
    "            doubleList3.append(v)\n",
    "            doublePop3 += tractPop3[v]\n",
    "    for m in countyMuniList[mC]:\n",
    "        if m not in muniPool3 and muniTractList[m][0] not in LISTS3[0]:  #remember, straddler districts didn't split mC muni's\n",
    "            muniPool3.append(m)\n",
    "    if not (isContiguous(doubleList,neighborList) and isContiguous(doubleList3,neighborList)) :\n",
    "        print(t,\" 's straddler district left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\")\n",
    "        continue\n",
    "\n",
    "    #for i, HL in enumerate(halfList):  #revised Dayton code -- here, only one more cut to make, not 2 halves\n",
    "    HL = doubleList.copy()  #reusing Dayton nomenclature\n",
    "    off2Pop = abs(0.5*doublePop - aDP)\n",
    "    TOL = max( 0.02*aDP, toler - 0.5*off2Pop)  #tighten if 2-district off\n",
    "    qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "    minAngle, maxAngle = -0.5*math.pi, 0.   \n",
    "    \n",
    "    qAngle0 = random.uniform(minAngle, maxAngle)  #based on Lorain shape: horizontal, vertical, or NW-SE cut may work\n",
    "    #3/16/23 - stopping here for night.  Could try multiple angles here -- did we do this in Dayton?\n",
    "    # need to figure out how to stop red death for list3B\n",
    "    \n",
    "    bestDlists = [list(), list(), list(), list()]\n",
    "    nAngleTries,lowScore = 10, 999999.\n",
    "    qLoopNo = 0\n",
    "    foundGoodAngle = False\n",
    "    while qLoopNo < nAngleTries:  #try multiple angles, save the districts with lowest score if all districts legit\n",
    "        qLoopNo +=1\n",
    "        qAngle = qAngle0 + math.pi * float(qLoopNo)/nAngleTries\n",
    "        dblList, dblPop = [list(), list()], [0., 0.]\n",
    "        dblList[0], dblPop[0] = cutVtdSnap(0.5*doublePop, TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)\n",
    "        for tt in HL:\n",
    "            if tt not in dblList[0]:  #throw all remaining vtds into 2nd whole Lorain district\n",
    "                dblList[1].append(tt)\n",
    "        dblPop[1] = doublePop - dblPop[0]\n",
    "\n",
    "        HL3 = doubleList3.copy()  #reusing Dayton code\n",
    "        dblList3, dblPop3 = [list(), list()], [0., 0.]\n",
    "        qCutPoint3 = get_PCP(HL3, tractCP, tractPop3)  #note: reusing same angle as vtd-snapped (horiz,diag or vert)\n",
    "        off2Pop3 = abs(0.5*doublePop3 - aDP)\n",
    "        TOL3 = max( 0.02*aDP, toler - 0.5*off2Pop3)  #tighten if 2-district off\n",
    "        dblList3[0], dML, dblPop3[0], splitMuni = cutMuniSnap(0.5*doublePop3, TOL3, muniTractList,tractCP, HL3,\n",
    "                                                              muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "        popGap = 0.5*doublePop3 - dblPop3[0]\n",
    "        list3B, pop3B = list(),0.\n",
    "        if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "            hT = getFarthestTract(qCutPoint3, dblList3[0], tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "            list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,dML,\n",
    "                                                      muniPool3,tractCP, neighborList,tractPop3,0)\n",
    "        dblList3[0], dblPop3[0] = dblList3[0] + list3B, dblPop3[0] + pop3B\n",
    "        for tt in HL3:\n",
    "            if tt not in dblList3[0]:  #throw all remaining vtds into 3rd whole Lucas district\n",
    "                dblList3[1].append(tt)\n",
    "        dblPop3[1] = doublePop3 - dblPop3[0]\n",
    "        isJiggy = True\n",
    "        score = 0.\n",
    "        currentLists,currentPops = [dblList[0], dblList[1], dblList3[0], dblList3[1]] ,[dblPop[0],dblPop[1], dblPop3[0], dblPop3[1] ]\n",
    "        for L in currentLists:\n",
    "            score += compactScore(L,tractCP, tractPop3)\n",
    "            if not isContiguous(L, neighborList):\n",
    "                isJiggy = False\n",
    "        for p in currentPops :\n",
    "            if abs(p - aDP) > toler:\n",
    "                isJiggy = False\n",
    "        if isJiggy and score < lowScore:\n",
    "            foundGoodAngle = True\n",
    "            lowScore = score\n",
    "            bestDlists = currentLists.copy()\n",
    "            bestDpops =  currentPops.copy()\n",
    "            bestAngle = qAngle\n",
    "    \n",
    "    if foundGoodAngle:  #overwrite.  If we didn't overwrite, we'll catch the fails in the nGood block\n",
    "        dblList[0], dblList[1], dblPop[0], dblPop[1] = bestDlists[0], bestDlists[1], bestDpops[0], bestDpops[1]\n",
    "        dblList3[0], dblList3[1], dblPop3[0], dblPop3[1] = bestDlists[2], bestDlists[3], bestDpops[2], bestDpops[3]\n",
    "    for i, L in enumerate(dblList3):\n",
    "        LISTS.append(dblList[i])\n",
    "        LISTS3.append(dblList3[i])\n",
    "        LISTpops.append(dblPop[i])\n",
    "        LISTpops3.append(dblPop3[i])\n",
    "    nGood, nGood3 = 0,0\n",
    "    for i, L in enumerate(LISTS):\n",
    "        if abs(LISTpops[i] - aDP) <= 0.05 * aDP and isContiguous(LISTS[i],neighborList):\n",
    "            nGood +=1\n",
    "        else:\n",
    "            print(t,i,LISTpops[i],isContiguous(LISTS[i],neighborList),\"t,i,vtd-pop,contiguity = FAIL\")\n",
    "        if abs(LISTpops3[i] - aDP) <= 0.05 * aDP and isContiguous(LISTS3[i],neighborList):\n",
    "            nGood3 +=1\n",
    "        else:\n",
    "            print(t,i,LISTpops3[i],isContiguous(LISTS3[i],neighborList),\"t,i,muni-pop,contiguity = FAIL\")\n",
    "            \n",
    "    if nGood == 3 and nGood3 == 3:\n",
    "        drawUnsuccessful = False\n",
    "        print(\"    \",t,\"has drawn a GOOD straddler +2-district Lorain; add to growing list\")       \n",
    "        Lorain3LISTS.append(LISTS)\n",
    "        Lorain3LISTS3.append(LISTS3)\n",
    "        Lorain3Starters.append(t)\n",
    "        LorainStraddlerAngles.append(mCangle)\n",
    "        LorainCutAngles.append(bestAngle) \n",
    "    else:\n",
    "        print(\"Boo! We only generated\",nGood, nGood3,\"of 3 legal districts for vtd-, muni-snap for vtd\",t )\n",
    "        badLorainStraddlerAngles.append(mCangle)\n",
    "print(\"all done for main county\",mC,\"we generated a total of\",len(Lorain3Starters),\n",
    "      \"good plans out of possible\",len(countyStraddlerList[mC]) )    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "acc526c3-d632-4ab3-b19a-32ea534ecaeb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c3ed4e88-a615-4329-a9df-7ea41a114448",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OK, it looks like we got a decent spread in angles.  Call Lorain DONE 3/18/23 after we add voting\n",
      "circles triggered good districts, x's failed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "of the 24 46 -driven plans 20 used county 21 and 4 used other county\n"
     ]
    }
   ],
   "source": [
    "print(\"OK, it looks like we got a decent spread in angles.  Call Lorain DONE 3/18/23 after we add voting\")\n",
    "plotPoly(countyGeom[mC])\n",
    "for v in countyStraddlerList[mC]:\n",
    "    if v in Lorain3Starters:\n",
    "        plotCenter(\"o\",tractCP[v])\n",
    "    else:\n",
    "        plotCenter(\"x\",tractCP[v])\n",
    "print(\"circles triggered good districts, x's failed\")\n",
    "plotCenter(\"PCP\",countyPCP[mC])\n",
    "plt.show()\n",
    "plt.scatter(LorainStraddlerAngles, LorainCutAngles)\n",
    "for a in badLorainStraddlerAngles:\n",
    "    plt.scatter(a,2.5,marker=\"x\")\n",
    "plt.xlabel(\"straddler cut angle\")\n",
    "plt.ylabel(\"2-district cut angle\")\n",
    "plt.show()\n",
    "nPPC = [0,0]\n",
    "for L in Lorain3LISTS:\n",
    "    for i,ppC in enumerate(pairedCountyList):\n",
    "        for v in L[0]:\n",
    "            if v in countyTractList[ppC]:\n",
    "                nPPC[i] +=1\n",
    "                break\n",
    "print(\"of the\",len(Lorain3Starters),mC,\"-driven plans\",nPPC[0],\"used county\",pairedCountyList[0],\"and\",nPPC[1],\"used other county\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "e31112e7-0a58-49cf-960b-e0580fad7cee",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "mC = 46 #main county = Lorain\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "pairedCountyList = [21,38] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "efce72cc-9299-40fa-8381-0f001958b954",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding Lorain voting here\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting done.  The most popular plan was 18 with 0.163 of the vote\n",
      "Voting done.  The most popular plan was 18 with 0.203 of the vote\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above is vtd-snapped plan weight, below is muni-snapped\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Adding Lorain voting here\")\n",
    "LorainAreaVoters, LorainAreaVoters3 = countyTractList[mC].copy(), countyTractList[mC].copy()\n",
    "for ppC in pairedCountyList:\n",
    "    altCPlist = [countyPCP[ppC] for v in countyTractList[ppC] ]\n",
    "    ppCvoters  = getPickyPCvoters(countyTractList[ppC], altCPlist, tractCP, tractPop, Lorain3LISTS)\n",
    "    ppCvoters3 = getPickyPCvoters(countyTractList[ppC], altCPlist, tractCP, tractPop, Lorain3LISTS3)\n",
    "    LorainAreaVoters += ppCvoters\n",
    "    LorainAreaVoters3+= ppCvoters3\n",
    "    for v in countyTractList[ppC]:\n",
    "        if v in ppCvoters:\n",
    "            plt.text(tractCP[v].x, tractCP[v].y,\"IN\")\n",
    "        else:\n",
    "            plt.text(tractCP[v].x, tractCP[v].y,\"X\")\n",
    "        if v in ppCvoters3:\n",
    "            plt.text(tractCP[v].x, tractCP[v].y+0.008,\"in3\",fontsize=6)\n",
    "    plotPoly(countyGeom[ppC])\n",
    "plt.show()\n",
    "\n",
    "LorainWeights =  setPlanWeights(LorainAreaVoters,  Lorain3LISTS, tractCP, tractPop3)\n",
    "LorainWeights3 = setPlanWeights(LorainAreaVoters3, Lorain3LISTS3, tractCP, tractPop3)\n",
    "for i,v in enumerate(Lorain3Starters):\n",
    "    if LorainWeights[i] == 0:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,\"o\",fontsize=4)\n",
    "    else:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,r3(LorainWeights[i]),fontsize=8)\n",
    "    if LorainWeights[i] == np.max(LorainWeights):\n",
    "        for j,L in enumerate(Lorain3LISTS[i]):\n",
    "            dGeom = tractGeom[L[0]]\n",
    "            for t in L:\n",
    "                dGeom = dGeom.union(tractGeom[t])\n",
    "            plotPoly(dGeom)\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()\n",
    "print(\"above is vtd-snapped plan weight, below is muni-snapped\")\n",
    "for i,v in enumerate(Lorain3Starters):\n",
    "    if LorainWeights3[i] == 0:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,\"o\",fontsize=4)\n",
    "    else:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,r3(LorainWeights3[i]),fontsize=8)\n",
    "    if LorainWeights3[i] == np.max(LorainWeights3):\n",
    "        for j,L in enumerate(Lorain3LISTS3[i]):\n",
    "            dGeom = tractGeom[L[0]]\n",
    "            for t in L:\n",
    "                dGeom = dGeom.union(tractGeom[t])\n",
    "            plotPoly(dGeom)\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "4dd9ea1c-f8d7-47fa-8417-360090862cb5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the HD weights to a file for region N-Lorain\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the HD weights to a file for region\",regionName[currentRegionNo])\n",
    "date = \"19MarREDO\"\n",
    "rLISTS, rWeights = [Lorain3LISTS.copy(), Lorain3LISTS3.copy()], [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+regionName[currentRegionNo]+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "ce6202ea-76fc-4b95-a5ed-4433ab830fdf",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On to Delaware\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"On to Delaware\")\n",
    "mC = 20 #main county = Delaware \n",
    "currentRegionNo = 5\n",
    "pairedCountyList = [50, 58, 79]  #Morrow, Marion, Union  (Knox-Licking already a committed pair, Franklin is whole]\n",
    "altPairingList = [ [16,79, 87], [16, 50], [10, 45, 48, 50]]  #I don't think this is needed  \n",
    "ppC = -999  #a default flag to not draw a whole district in any neighboring counties\n",
    "#ppC = pairedCountyList[0]  #primary paired county (e.g. Wood = 86)   #n/a for this region\n",
    "#ppCmaxPop = countyPop[ppC] % avgDistrictPop  #the max population from the primary pairing county that can be used in a straddling district\n",
    "targetRemnantPop = countyPop[mC]%avgDistrictPop  #the natural pop left after filling whole districts in main county\n",
    "luringCountyList = pairedCountyList.copy()\n",
    "luringList = list()\n",
    "forcedTractList = [ ]  #no forcing tracts needed for Delaware; will find the north / west tracts to be our straddlers\n",
    "\n",
    "for c in luringCountyList:\n",
    "    for tt in countyTractList[c]:\n",
    "        luringList.append(tt)   #these tracts in nearby counties \"lure\" HDs which contain them to center straddler HDs\n",
    "forcedGeom = countyGeom[mC]   #default; we do not force any tracts to be in the original HDPoly's of straddler districts\n",
    "if len(forcedTractList) > 0:\n",
    "    forcedGeom = tractCP[forcedTractList[0]]\n",
    "    for T in forcedTractList:\n",
    "        forcedGeom = forcedGeom.union(tractCP[T])\n",
    "\n",
    "countyStraddlerList[mC] = get_straddlers(targetRemnantPop, countyPop[mC], countyTractList[mC],\n",
    "                                         luringList, forcedGeom, HDPoly, tractCP, tractPop3)\n",
    "mC_EnsnaredPop = 0.\n",
    "for t in countyTractList[mC]:\n",
    "    if t in countyStraddlerList[mC]:\n",
    "        HDtype[t] = \"straddlerMaker\"        \n",
    "        plt.text(HDCPx[t],HDCPy[t],t, fontsize=6)\n",
    "        mC_EnsnaredPop += tractPop3[t]\n",
    "    else:\n",
    "        HDtype[t] = \"notAstraddlerMaker\"\n",
    "        plt.scatter(HDCPx[t],HDCPy[t],marker=\".\", label=\"non-straddler\")\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "00a702b3-c4f5-4b76-9eaf-4a2162cfbfb8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now build the HDtractList (and the resulting regional plan) for each possible straddler HD spawned in county 20\n",
      "t,mCsnapPop, mCsnapPop3 are 5418 96346.0 89662.0 vs tgt ICP 94937.65656565657\n",
      "5418 1 113241.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5418\n",
      "t,mCsnapPop, mCsnapPop3 are 5334 95693.0 96040.0 vs tgt ICP 94937.65656565657\n",
      "5334 1 124462.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5334\n",
      "t,mCsnapPop, mCsnapPop3 are 5339 95286.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5339 1 118084.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5339\n",
      "t,mCsnapPop, mCsnapPop3 are 5415 96275.0 89662.0 vs tgt ICP 94937.65656565657\n",
      "5415 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5415\n",
      "t,mCsnapPop, mCsnapPop3 are 5333 96089.0 89662.0 vs tgt ICP 94937.65656565657\n",
      "5333 1 124462.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5333\n",
      "t,mCsnapPop, mCsnapPop3 are 5321 97140.0 91374.0 vs tgt ICP 94937.65656565657\n",
      "5321 1 124462.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5321\n",
      "t,mCsnapPop, mCsnapPop3 are 5331 97021.0 93650.0 vs tgt ICP 94937.65656565657\n",
      "     5331 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5414 95581.0 98608.0 vs tgt ICP 94937.65656565657\n",
      "     5414 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5431 93730.0 98204.0 vs tgt ICP 94937.65656565657\n",
      "     5431 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5338 97140.0 90450.0 vs tgt ICP 94937.65656565657\n",
      "     5338 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5353 93048.0 99801.0 vs tgt ICP 94937.65656565657\n",
      "5353 1 123674.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5353\n",
      "t,mCsnapPop, mCsnapPop3 are 5281 96016.0 91285.0 vs tgt ICP 94937.65656565657\n",
      "5281 1 114323.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5281\n",
      "t,mCsnapPop, mCsnapPop3 are 5285 97112.0 98608.0 vs tgt ICP 94937.65656565657\n",
      "5285 1 122839.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5285\n",
      "t,mCsnapPop, mCsnapPop3 are 5296 97215.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "     5296 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 6262 94261.0 99801.0 vs tgt ICP 94937.65656565657\n",
      "6262 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 6262\n",
      "t,mCsnapPop, mCsnapPop3 are 5354 96121.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5354 1 114323.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5354\n",
      "t,mCsnapPop, mCsnapPop3 are 5294 96345.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5294 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5294\n",
      "t,mCsnapPop, mCsnapPop3 are 5288 94704.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5288 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5288\n",
      "t,mCsnapPop, mCsnapPop3 are 5295 93643.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5295 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5295\n",
      "t,mCsnapPop, mCsnapPop3 are 5276 97112.0 98608.0 vs tgt ICP 94937.65656565657\n",
      "5276 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5276\n",
      "t,mCsnapPop, mCsnapPop3 are 5345 93835.0 89662.0 vs tgt ICP 94937.65656565657\n",
      "     5345 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5287 95419.0 93650.0 vs tgt ICP 94937.65656565657\n",
      "5287 1 124462.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5287\n",
      "t,mCsnapPop, mCsnapPop3 are 5332 94893.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "     5332 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5350 95286.0 91285.0 vs tgt ICP 94937.65656565657\n",
      "5350 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5350\n",
      "t,mCsnapPop, mCsnapPop3 are 5284 94704.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5284 1 122839.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5284\n",
      "t,mCsnapPop, mCsnapPop3 are 5263 96784.0 98204.0 vs tgt ICP 94937.65656565657\n",
      "5263 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5263\n",
      "t,mCsnapPop, mCsnapPop3 are 6551 96653.0 96040.0 vs tgt ICP 94937.65656565657\n",
      "     6551 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5286 93010.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5286 1 118084.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5286\n",
      "t,mCsnapPop, mCsnapPop3 are 5291 97140.0 91374.0 vs tgt ICP 94937.65656565657\n",
      "5291 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5291\n",
      "t,mCsnapPop, mCsnapPop3 are 5272 94752.0 99554.0 vs tgt ICP 94937.65656565657\n",
      "     5272 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5309 94563.0 94462.0 vs tgt ICP 94937.65656565657\n",
      "5309 1 114570.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5309\n",
      "t,mCsnapPop, mCsnapPop3 are 5436 97111.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5436 1 119662.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5436\n",
      "t,mCsnapPop, mCsnapPop3 are 5273 96382.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "5273 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5273\n",
      "t,mCsnapPop, mCsnapPop3 are 5293 96371.0 90450.0 vs tgt ICP 94937.65656565657\n",
      "     5293 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5274 93730.0 89662.0 vs tgt ICP 94937.65656565657\n",
      "5274 1 123674.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5274\n",
      "t,mCsnapPop, mCsnapPop3 are 5268 95693.0 96040.0 vs tgt ICP 94937.65656565657\n",
      "5268 1 124462.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5268\n",
      "t,mCsnapPop, mCsnapPop3 are 5289 96121.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5289 1 118084.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5289\n",
      "t,mCsnapPop, mCsnapPop3 are 5417 92862.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5417 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5417\n",
      "t,mCsnapPop, mCsnapPop3 are 5366 95611.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5366 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5366\n",
      "t,mCsnapPop, mCsnapPop3 are 5337 93565.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5337 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5337\n",
      "t,mCsnapPop, mCsnapPop3 are 5277 95778.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5277 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5277\n",
      "t,mCsnapPop, mCsnapPop3 are 5283 94075.0 99801.0 vs tgt ICP 94937.65656565657\n",
      "5283 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5283\n",
      "t,mCsnapPop, mCsnapPop3 are 5420 96382.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "5420 1 114323.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5420\n",
      "t,mCsnapPop, mCsnapPop3 are 5443 93219.0 94678.0 vs tgt ICP 94937.65656565657\n",
      "     5443 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5271 95146.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "5271 1 119446.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5271\n",
      "t,mCsnapPop, mCsnapPop3 are 5413 96737.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "     5413 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5421 96382.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "5421 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5421\n",
      "t,mCsnapPop, mCsnapPop3 are 5432 94893.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "     5432 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5269 94364.0 99554.0 vs tgt ICP 94937.65656565657\n",
      "5269 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5269\n",
      "t,mCsnapPop, mCsnapPop3 are 5282 97140.0 90450.0 vs tgt ICP 94937.65656565657\n",
      "5282 1 114570.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5282\n",
      "t,mCsnapPop, mCsnapPop3 are 5270 96382.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "5270 1 123674.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5270\n",
      "t,mCsnapPop, mCsnapPop3 are 5369 96346.0 92021.0 vs tgt ICP 94937.65656565657\n",
      "     5369 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5352 95286.0 91285.0 vs tgt ICP 94937.65656565657\n",
      "5352 1 122103.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5352\n",
      "t,mCsnapPop, mCsnapPop3 are 5336 93076.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5336 1 122839.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5336\n",
      "t,mCsnapPop, mCsnapPop3 are 5419 96780.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5419 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5419\n",
      "t,mCsnapPop, mCsnapPop3 are 5433 94956.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5433 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5433\n",
      "t,mCsnapPop, mCsnapPop3 are 5290 97215.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5290 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5290\n",
      "t,mCsnapPop, mCsnapPop3 are 5340 94136.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "5340 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5340\n",
      "t,mCsnapPop, mCsnapPop3 are 5416 96382.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "     5416 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 3772 93669.0 89662.0 vs tgt ICP 94937.65656565657\n",
      "     3772 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5278 94331.0 91374.0 vs tgt ICP 94937.65656565657\n",
      "5278 1 124462.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5278\n",
      "t,mCsnapPop, mCsnapPop3 are 5341 96382.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "     5341 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5435 96113.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "     5435 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5335 96113.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "5335 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5335\n",
      "t,mCsnapPop, mCsnapPop3 are 5292 93470.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "5292 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5292\n",
      "t,mCsnapPop, mCsnapPop3 are 6261 93724.0 98608.0 vs tgt ICP 94937.65656565657\n",
      "6261 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 6261\n",
      "t,mCsnapPop, mCsnapPop3 are 5399 93948.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "     5399 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 3769 93622.0 91045.0 vs tgt ICP 94937.65656565657\n",
      "3769 1 120176.0 False t,i,vtd-pop,contiguity = FAIL\n",
      "Boo! We only generated 1 2 of 2 legal districts for vtd-, muni-snap for vtd 3769\n",
      "t,mCsnapPop, mCsnapPop3 are 5262 94810.0 94462.0 vs tgt ICP 94937.65656565657\n",
      "5262 1 123079.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5262\n",
      "t,mCsnapPop, mCsnapPop3 are 5221 96179.0 98204.0 vs tgt ICP 94937.65656565657\n",
      "5221 1 119662.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5221\n",
      "t,mCsnapPop, mCsnapPop3 are 5314 96382.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "     5314 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5279 95286.0 95535.0 vs tgt ICP 94937.65656565657\n",
      "     5279 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 476 94514.0 94462.0 vs tgt ICP 94937.65656565657\n",
      "476 1 118589.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 476\n",
      "t,mCsnapPop, mCsnapPop3 are 475 95146.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "475 1 119662.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 475\n",
      "t,mCsnapPop, mCsnapPop3 are 5280 96290.0 91285.0 vs tgt ICP 94937.65656565657\n",
      "     5280 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "t,mCsnapPop, mCsnapPop3 are 5365 96804.0 100883.0 vs tgt ICP 94937.65656565657\n",
      "5365 1 122839.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5365\n",
      "t,mCsnapPop, mCsnapPop3 are 5275 95146.0 100275.0 vs tgt ICP 94937.65656565657\n",
      "5275 1 113241.0 False t,i,muni-pop,contiguity = FAIL\n",
      "Boo! We only generated 2 1 of 2 legal districts for vtd-, muni-snap for vtd 5275\n",
      "t,mCsnapPop, mCsnapPop3 are 5434 95983.0 94678.0 vs tgt ICP 94937.65656565657\n",
      "     5434 has drawn a GOOD straddler +1-district Delaware; add to growing list\n",
      "all done for main county 20 we generated a total of 23 good plans out of possible 78\n"
     ]
    }
   ],
   "source": [
    "print(\"I will now build the Delaware HDtractList (and the resulting regional plan) for each possible straddler HD spawned in county\",mC)\n",
    "stdCutPoint = Point(-83.02,40.15)  #south center of Delaware to orient all pie cuts (PCP might be too far north to work well)\n",
    "Delaware2LISTS = list()  #a list of 3 lists of vtds in a 3-district area vtd-snapped regional plan\n",
    "Delaware2LISTS3 = list()  #same, for muni-snapped\n",
    "Delaware2Starters = list() #the starter vtd No for the two above lists\n",
    "DelawareAngles = list()  #FYI, the cut angle for making the in-DE straddler\n",
    "badDelawareAngles = list()  #FYI, for those straddler-initiators that failed to make the cut!\n",
    "angleNoise = 0.05   #we'll add some wobble to straddler cut angles to get better sampling, hopefully\n",
    "toler = 0.05*aDP\n",
    "      \n",
    "for t in countyStraddlerList[mC]:  #obtained in above block\n",
    "    homeM = tractMuniNo[t]\n",
    "    nSplits = 0  #tracking total splits in all counties composing this straddler district.  Force to 0-1\n",
    "    HDangle = [HDangle0[t],  HDangle1[t], HDangle2[t], HDangle3[t] ]\n",
    "    HDrad4 = [4.*HDradius0[t], 4.*HDradius1[t], 4.*HDradius2[t], 4.*HDradius3[t] ]\n",
    "    targetICP = countyPop[mC]%aDP  #target in-county Pop.  reset in case we adjusted for previous tract\n",
    "    adjCountyHDPolyPop = [0.] * nCounties  #for Montgomery, we need special rules based on which adjacent counties we spill into\n",
    "    for cc in pairedCountyList:\n",
    "        for tt in countyTractList[cc]:\n",
    "            overlap = HDPoly[t].intersection(tractGeom[tt]).area\n",
    "            if overlap > 0:\n",
    "                adjCountyHDPolyPop[cc] += tractPop[tt] * overlap / tractGeom[tt].area\n",
    "    ppC = adjCountyHDPolyPop.index(np.max(adjCountyHDPolyPop) ) #the primary pairing county had the most pop in this t's orig HD\n",
    "    #spC = spcGroups[pairedCountyList.index(ppC)]  #for Delaware, no spC needed   \n",
    "    mCangle = getSliceAngle(tractCP[t],stdCutPoint,tractCP[t]) + random.uniform(-1.*angleNoise,angleNoise)\n",
    "    #print(r3(180./3.1416*mCangle), \"is our computed slice angle in deg for tract\",t)\n",
    "    #plotPoly(angledPentaPoly(stdCutPoint,mCangle,stdCutPoint.distance(tractCP[t]) ) )\n",
    "    #plotCenter(\"sCP\",stdCutPoint)\n",
    "    #plotPoly(tractCP[t].buffer(0.01))\n",
    "    #plotPoly(countyGeom[mC])\n",
    "    #plt.show()\n",
    "    mClist, mCsnapPop = cutVtdSnap(targetICP, toler, tractCP, countyTractList[mC],neighborList,tractPop3, mCangle, countyPCP[mC],t)\n",
    "    #tried cut HDvtdSnap and HDmuniSnap but they often left discontiguous 2-district remnants\n",
    "    mClist3, mcML3, mCsnapPop3, splitMuni = cutMuniSnap(targetICP, toler, muniTractList, tractCP, countyTractList[mC],\n",
    "                                                        muniNeighborList,tractPop3, muniPop, muniPCP, mCangle, countyPCP[mC])\n",
    "    popGap = targetICP - mCsnapPop3\n",
    "    mClist3B, mCsnapPop3B = list(),0.\n",
    "    if (abs(popGap) > toler) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "        if popGap < 0  :\n",
    "            raise Exception(\"error! mCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "        mClist3B, mCsnapPop3B = getFillList3noSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                    mcML3, countyMuniList[mC],tractCP, neighborList,tractPop3)\n",
    "        if mCsnapPop3B == 0. :\n",
    "            print(\"uh oh, failed to create whole-muni in inCounty part of straddler district for tract\",t,\"we are short by\",popGap)\n",
    "        \n",
    "        mClist3, mCsnapPop3 = mClist3 + mClist3B, mCsnapPop3 + mCsnapPop3B  #completing in the inCounty partial despite splitM flag\n",
    "    \n",
    "    snappedHDpop[t] = mCsnapPop\n",
    "    snappedHD3pop[t] = mCsnapPop3\n",
    "    HDtractList[t]  = mClist.copy()\n",
    "    HDtractList3[t] = mClist3.copy()\n",
    "    print(\"t,mCsnapPop, mCsnapPop3 are\",t,mCsnapPop, mCsnapPop3,\"vs tgt ICP\",targetICP)\n",
    "    if countyPop[ppC] > 1.05* aDP - mCsnapPop:\n",
    "        isFullppC, isFullppC3 = False, False  #always true for DE.  See Dayton for adding full ppC + part spC\n",
    "    ppCcT = getClosestTract(tractCP[t],countyTractList[ppC],tractCP)\n",
    "    ppCangle = getSliceAngle(tractCP[ppCcT],countyPCP[ppC],tractCP[ppCcT])\n",
    "    if not isFullppC:\n",
    "        ppCtgtPop = aDP - mCsnapPop\n",
    "        ppClist, ppCsnapPop = cutVtdSnap(ppCtgtPop, toler, tractCP, countyTractList[ppC],neighborList,tractPop3,\n",
    "                                        ppCangle, countyPCP[ppC]) \n",
    "    if not isFullppC3:\n",
    "        ppCtgtPop3 = aDP - mCsnapPop3\n",
    "        ppClist3, ppcML3, ppCsnapPop3, splitMuni = cutMuniSnap(ppCtgtPop3, toler, muniTractList, tractCP, countyTractList[ppC],\n",
    "                                                             muniNeighborList,tractPop3, muniPop, muniPCP, ppCangle, countyPCP[ppC])  \n",
    "        popGap = ppCtgtPop3 - ppCsnapPop3\n",
    "        #print(t,int(ppCtgtPop3), ppCsnapPop3, \"t,tgt ppC3, actual\",ppcML3,\"=ppcML3\")\n",
    "        if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "            if popGap < 0  :\n",
    "                print(\"munisnap pop of\",mCsnapPop3,\"in\",mC,\"and\",ppCsnapPop3,\"in cty\",ppC,\"vs tgt\",ppCtgtPop3)\n",
    "                raise Exception(\"error! ppCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            # 3/4/23 NOTE:  WE COULD TRY A WORKAROUND IF ppcML3 is blank.  Requires us to ID all ppC muni's contiguous to mC HDlist\n",
    "            ppClist3B, ppCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,ppcML3,\n",
    "                                                        countyMuniList[ppC],tractCP, neighborList,tractPop3,0)        \n",
    "            ppClist3, ppCsnapPop3 = ppClist3 + ppClist3B, ppCsnapPop3 + ppCsnapPop3B\n",
    "        #print(t,ppCsnapPop, ppCsnapPop3,\"ppC pops vs tgts\",int(ppCtgtPop), int(ppCtgtPop3))\n",
    "    \n",
    "    HDtractList[t]  += ppClist\n",
    "    HDtractList3[t] += ppClist3\n",
    "    snappedHDpop[t]  += ppCsnapPop\n",
    "    snappedHD3pop[t] += ppCsnapPop3\n",
    "\n",
    "    #print(\"after adding ppC, t, HDpop and pop3 are\",t, snappedHDpop[t], snappedHD3pop[t])\n",
    "    # deleted all spC code here -- not needed for Lorain\n",
    "    doubleList, doubleList3, doublePop, doublePop3 = list(), list(), 0., 0.\n",
    "    for v in countyTractList[mC]:\n",
    "        if v not in LISTS[0]:\n",
    "            doubleList.append(v)\n",
    "            doublePop  += tractPop3[v]\n",
    "    muniPool3 = list()  #the list of munis in remaining 2-district set (we did not allow splitMuni in prev cut)\n",
    "    for v in countyTractList[mC]:\n",
    "        if v not in LISTS3[0]:\n",
    "            doubleList3.append(v)\n",
    "            doublePop3 += tractPop3[v]\n",
    "    LISTS = [  HDtractList[t], doubleList ]\n",
    "    LISTS3 = [ HDtractList3[t], doubleList3 ]\n",
    "    LISTpops =  [snappedHDpop[t], doublePop ]\n",
    "    LISTpops3 = [snappedHD3pop[t], doublePop3 ]\n",
    "    nGood, nGood3 = 0, 0\n",
    "    for i, L in enumerate(LISTS):\n",
    "        if abs(LISTpops[i] - aDP) <= 0.05 * aDP and isContiguous(LISTS[i],neighborList):\n",
    "            nGood +=1\n",
    "        else:\n",
    "            print(t,i,LISTpops[i],isContiguous(LISTS[i],neighborList),\"t,i,vtd-pop,contiguity = FAIL\")\n",
    "        if abs(LISTpops3[i] - aDP) <= 0.05 * aDP and isContiguous(LISTS3[i],neighborList):\n",
    "            nGood3 +=1\n",
    "        else:\n",
    "            print(t,i,LISTpops3[i],isContiguous(LISTS3[i],neighborList),\"t,i,muni-pop,contiguity = FAIL\")\n",
    "            \n",
    "    if nGood == 2 and nGood3 == 2:\n",
    "        drawUnsuccessful = False\n",
    "        print(\"    \",t,\"has drawn a GOOD straddler +1-district Delaware; add to growing list\")       \n",
    "        Delaware2LISTS.append(LISTS)\n",
    "        Delaware2LISTS3.append(LISTS3)\n",
    "        Delaware2Starters.append(t)\n",
    "        DelawareAngles.append(mCangle)\n",
    "    else:\n",
    "        print(\"Boo! We only generated\",nGood, nGood3,\"of 2 legal districts for vtd-, muni-snap for vtd\",t )\n",
    "        badDelawareAngles.append(mCangle)\n",
    "print(\"all done for main county\",mC,\"we generated a total of\",len(Delaware2Starters),\n",
    "      \"good plans out of possible\",len(countyStraddlerList[mC]) )    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "1b126376-a1ce-4eef-b29f-f26c8fb9a545",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for m in countyMuniList[mC]:\n",
    "    plotPoly(muniGeom[m])\n",
    "    if muniPop[m] > 10000:\n",
    "        plotCenter(muniPop[m],muniGeom[m])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "201f0102-d4b3-4a3d-bc84-cb175ce465f2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "How was our spread in usable angles for county 20\n",
      "circles triggered good districts, x's failed\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "of the 23 plans in county 20 , 6 ,used county 50 and 8 used 2nd county\n"
     ]
    }
   ],
   "source": [
    "print(\"How was our spread in usable angles for county\",mC)\n",
    "plotPoly(countyGeom[mC])\n",
    "for v in countyStraddlerList[mC]:\n",
    "    if v in Delaware2Starters:\n",
    "        plotCenter(\"o\",tractCP[v])\n",
    "    else:\n",
    "        plotCenter(\"x\",tractCP[v])\n",
    "print(\"circles triggered good districts, x's failed\")\n",
    "plotCenter(\"PCP\",countyPCP[mC])\n",
    "plt.show()\n",
    "for i, t in enumerate(Delaware2Starters):\n",
    "    plt.scatter(tractCP[t].x, DelawareAngles[i])\n",
    "for a in badDelawareAngles:\n",
    "    plt.scatter(-83,a,marker=\"x\")\n",
    "plt.xlabel(\"longitude\")\n",
    "plt.ylabel(\"2-district cut angle\")\n",
    "plt.show()\n",
    "nPPC = [0 for p in pairedCountyList]\n",
    "for L in Delaware2LISTS:\n",
    "    for i,ppC in enumerate(pairedCountyList):\n",
    "        for v in L[0]:\n",
    "            if v in countyTractList[ppC]:\n",
    "                nPPC[i] +=1\n",
    "                break\n",
    "print(\"of the\",len(Delaware2Starters),\"plans in county\",mC,\",\",nPPC[0],\",used county\",pairedCountyList[0],\"and\",nPPC[1],\"used 2nd county\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "fa94f713-893f-4729-936b-05bec8810c60",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adding Delaware voting here.  county = 20\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting done.  The most popular plan was 14 with 0.169 of the vote\n",
      "Voting done.  The most popular plan was 9 with 0.171 of the vote\n"
     ]
    },
    {
     "data": {
      "image/png": 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9/KSslGlJSUzLTOEfdU4W/NcWnu5b0KSNh30+7ik7xEtD+5GRZGPB//w3v935T24bWYjfbueHl44g91e/4oorrmjxGrdv305iouyTJTqGhBoR01QttE2CrCgsukA41Jg75o+0oqooyemoyelQOLxDztkSvaGeYEUZwYrDBCuPEaw8TvmhEvat2MgL3x2PUlfPd5wuVr5cQWWSRrauEvQYYegfrjquSHSQ7DHj9QSZHufgD/uOMrXe+CBRaN6L5aZ5ndZ2IU4noUbEtFOVGvmnLjqf5nEC4I/rXpUHxWbH3HcI5r5Dwvd9vnMnuU/9k5zfvxC+r2DsWPTf/IYBl10GgO710LBwHsOSEuj7rcsJVpYzas8+Kh57ltTL+lP91pfoQeAMe69NmjQJv9/P5MmT+cUvfoHd3rzKJURbyUADEdPU0JohyJRu0QX0BiPUaN0s1LRGUZQm3+u63vRxixU1MYW4PgNIuOoWHDOXkjhzCWpKL1LnLgHAFE+rM7cOHTrEBx98wPbt2zlx4gQ/+clPOuU6RM8hoUbEtMZQI5Ua0SV8dQDo8Y4IN+T85eXlcfjwYQIBowtX13XKysrIz89vclx+fj4lJSXh7w8dOkR+fj7B6hMAqPFNg9FXnwtgt9tZtGgRb7/9dgdfhehpJNSImCYDhUVXUkOhRrF2/80sMzIyGDVqFFu2bAHg+eefp6CggIKCgibHTZ8+nRdffJHjx4+j6zqPPfYYM2bMQKs2Zjqp1parpNXV1bjdxj5Zmqbx7LPPMmrUqM67INEjSKgRMe1UqJHuJ9H5zH5j6rRq6/6hBmD9+vWsX7+eQYMG8eCDD7Jp0yYA5s6dy0svvQRA//79Wb58ORMnTqSwsJCMjAzmzJmDVnsSgKkffcn48eOprq6mT58+3HrrrQB89tlnXHzxxYwcOZLhw4dTVVXFmjVrInKdInZITV7ENJOMqRFdKC5ghBqTLTY2sxw8eDDvvvtus/sff/zxJt/PmzePefOaznBy1VYD8LevDyfvf/7d7Bzjx4/n448/7sDWCiGVGhHjVN0INWqcdD+JzhcfqAcgLiE2Qs350OqMQdOmBGuEWyJ6Egk1Iqad2vtJKjWi81k0I9TE21Mi25AoEKwzxhepdtnAU3QdCTUipjV2P6ky+0l0gQTNGPhqS0yJbEOigOYyAp6aKOvOiK4joUbEtMZQo8RJpUZ0rqCmYycUahwpkW1MFNDqjZ+FydH9p7eL7uO8Qs2qVatQFIUlS5aE79N1nfvvv5+cnBxsNhuTJk1iz549bT7nM888g6IoXH/99c0eW7duHf369cNqtTJ69GhZ00Ccka7rmEIbWp5pRVMhOoLL4yeRBgASklIj3JrIC7o9AKiO2JgJJrqHcw41O3bsYMOGDYwYMaLJ/atXr+ahhx5i7dq17Nixg6ysLK688krqQv2rZ3Lo0CF+/OMft7hL67PPPsuSJUu49957+fDDD7n00ku5+uqrKS0tPddLED2IKmNqRCerq3NiVowQbUlIiWxjooDm9gFgSpaAJ7rOOYUal8vFzJkz2bhxI6mpp/7B6rrOmjVruPfee7nhhhsYNmwYmzdvxu1289RTT53xnMFgkJkzZ7J8+XL69+/f7PGHHnqIOXPmMHfuXC644ALWrFlDXl4ejz766Llcguhh1NYXNRWiQ7jrjCnMGgrEyziSoMcPgJqcFuGWiJ7knELN4sWLueaaa5ptJX/w4EHKy8uZMmVK+D6LxcLll1/O9u3bz3jOBx54gN69ezNnzpxmj/l8Pnbu3NnkvABTpkw543m9Xi9Op7PJTfQgp+1ToyCpRnSuhroaANwkgCL/3jRPaJB+aq8It0T0JO0eaPDMM8+wa9cuduzY0eyx8vJyADIzM5vcn5mZyaFDh1o95zvvvMOmTZvYvXt3i49XVlYSDAZbPG/ja7Zk1apVLF++vNXHRc/x1Y35hOhonvoaANxqArGxneX50Xw6oGBK7R3ppogepF2VmrKyMu688062bNmC1dr6gkot7eza2h+Vuro6Zs2axcaNG+nV68yJvj3nBbjnnnuora0N38rKys54fhFjTqvUqBJqRCfz1dcC4DVJ15OuaQSNITWoaRmRbYzoUdpVqdm5cycVFRWMHj06fF8wGOStt95i7dq17N+/HzAqNtnZ2eFjKioqmlVZGn3xxReUlJRw7bXXhu/TQgummc1m9u/fT15eHiaTqVlV5kznBaPry2KxtOcSRYxSZJyw6GSBemNMjc8kdRrdeRJ044OEKT37LEcL0XHaVamZPHkyn3zyCbt37w7fxowZw8yZM9m9ezf9+/cnKyuLV199Nfwcn8/Hm2++yYQJE1o855AhQ5qdc+rUqXz9619n9+7d5OXlER8fz+jRo5ucF+DVV19t9bxCSKVGdCXdbWzg6I1LiWxDokCw6pjxhaKjJMuYGtF12lWpcTgcDBs2rMl9drud9PT08P1Llixh5cqVDBw4kIEDB7Jy5UoSEhK45ZZbws+ZPXs2ubm5rFq1CqvV2uycKSkpAE3uX7p0Kbfeeitjxoxh/PjxbNiwgdLSUhYuXNiuCxY9k4ypEZ1N8RiVGr8lJbINiQLayeMAqHGgqLLGq+g6Hb4i2bJly2hoaGDRokVUV1czbtw4tm7diuO0VSVLS0tR2/kP/aabbqKqqooHHniAY8eOMWzYMF555RX69u3b0ZcgYsXps58k1IhOZgqFmmCUhJri4mJuu+02KisrSUlJ4YknnuDCCy9sdtymTZt48MEH0TSNyZMns27dOsxmMyUlJQwYMKDJh8vnn3+ewsJC9u7d2+SDak1NDU6nk5MnjWqVdvIEACaL/N6JrqXo+mnv/DHO6XSSnJxMbW0tSUmyymWs0/1+PhtuLA4Z/8o/Kewvffui87z725sYX/d3dg74IaNn/SLSzeEb3/gGs2fP5vbbb+e5557jt7/9Le+++26TYw4ePMjEiRP58MMPycjI4LrrruOaa65hwYIFlJSUMGbMGCorK8/6Wj/4wQ9QFIWHH34YANezD1P283VYepnov+3TTrk+0bO09e+31AVFjyBjakRni/cbs5+UhMivoFtRUcGuXbuYNWsWANOnT+fgwYOUlJQ0Oe65555j2rRpZGZmoigKCxcu5Omnn27Xa3m9Xp566qkma4xptUbFxmSLO78LEaKdJNSI2CUDhUUXsgWMUGNOjPzA2LKyMnJycjCHdqdXFIX8/Pxm28qUlpY26cIvKChocozT6eRrX/saF110EQ888ADBYLDZa73wwgv069ePoqKi8H3BWqMrTrXFd+RlCXFWEmpEj6DIv3TRyeyasWK5OTE9wi0xtLSu19mOO/2Y7OxsDh8+zI4dO3jttdd4++23+e1vf9vs+X/4wx+arQSvhVZvVxNaX89MiM4gb/UiZp3+Fi6FGtHZErR6AOLtKZFtCJCXl8fhw4cJBAKAEVbKysrIz89vclx+fn6TLqlDhw6Fj7FYLGRkGAvnpaWl8b3vfY+33367yfMPHTrE9u3bmwwaBgi6jA2MTXZbh16XEGcjoUb0CO2dbSdEe9l0DwBWe3KEWwIZGRmMGjWKLVu2AMaspYKCAgoKCpocN336dF588UWOHz+Orus89thjzJgxAzDG5fj9xqaUXq+XF154gVGjRjV5/h//+EemTZsWXoajkVZnBDw1UVZXFl1L3ulF7Dp9TI1saBlWXFzMhAkTGDRoEGPHjmXv3r3NjikpKWHSpEkkJyczZsyYJo998sknXHbZZQwZMoThw4czf/58vF5v+HFFURgxYgRFRUUUFRU1+3Qfi3RNIwEj1FgSo2Nm5fr161m/fj2DBg3iwQcfZNOmTQDMnTuXl156CYD+/fuzfPlyJk6cSGFhIRkZGeGupG3btjFq1ChGjhzJRRddRFZWFvfee2/4/Lqu88QTT7S4CbFW7wbAlOho9pgQnUmmdIuYpXm97B9ZBEDav7aRmR0dYx0irS1TfU+ePMnevXupra3l5z//OR988EH4seLiYhoaGhgxYgTBYJBbbrmFkSNH8tOf/hQwQk1dXR2JiT1nu4AGlxPbb/IAcP+4lITEyFdrIqnsuotx7a8l644rSb3r95FujogBMqVbiNNJoQZo+1TftLQ0LrnkEuz25t0HAwcOZMQIY/0fk8nE1772Nb788stOb3s0c4c2s9R0Baut54S51mgNRuVOTU6JbENEjyOhRsQsPbQxKsiKwo3aOtW3rerr63n88cebbEgLMGnSJEaOHMnSpUupr68/73ZHO0+9MdunAQuqSXZPDTYYY3FMSZFfs0f0LBJqRMw6vWNVBgqf0tapvmfj9/u56aabmDJlCtddd134/kOHDvHBBx+wfft2Tpw4wU9+8pPzam934HEZocatyGwfAM1rrGejpkZ+zR7Rs8g7vYhZ2mmVGlUKNUDbp/qejd/v58YbbyQ7O5vf/e53TR5rPJfdbmfRokU9YqCwv8EINR4JNQBoXiMoqym9I9wS0dNIqBExSzutACHdT4a2TvU9k0AgwIwZM0hLS2PDhg1NfrbV1dW43cbMF03TePbZZ5tNA45FvgZjXRavKqFG1zSCPuNrU3pGZBsjehwJNSJ2nd6tIpkmrC1Tfb1eL3369OG73/0uH3/8MX369OGee+4B4Nlnn+WFF17ggw8+YNSoURQVFbF48WIAPvvsMy6++GJGjhzJ8OHDqaqqYs2aNRG5zq4UDIUan4Qa9Lpq0I1fODUtK8KtET2NOdINEKJrSKppNHjw4GZTuAEef/zx8NcWi4XDhw+3+PyZM2cyc+bMFh8bP348H3/8ccc0tBsJeIxQ4zcnRLglkRc8WR76SkdNkUqN6FpSqRExrMcswSQiTPO6AAiYJNRoVccBUONAMcvnZtG1JNSImHV6pFFlpLDoRHoo1ASlUoNWXQGAapHfOdH1JNQIIcT58hlr8WhxsvBesLoSAJNF1usRXU9CjYhdPWcHEBFhis+o1OjxsoGjVnsSANUmXU+i60moETFLlyndoouofmMaOxJqCNbWAGCyxUe2IaJHklAjYpdUakQXMQWM7iclXrqfNKexD5aaYIlwS0RPJKFG9AxSqRGdyBwwKjWK1RHhlkSeVmdMb1cTZNC06HoSakTskkqN6CJxQSPUmK1SqQm6jPFFpkTpihNdT0KN6BEU2dBStEFxcTETJkxg0KBBjB07lr1797Z43KZNmxg4cCCFhYXMnz8fk9/ofjLbjEqNrutMnjyZXr2abuhYXV3NzJkzGThwIBdccAF33313515QBGj1RsBTHRLwRNeTd3ohhAhZsGAB8+fP5/PPP2fZsmXMmTOn2TEHDx7kvvvuY9u2bRw4cIDy8nJe3GmsohufkATA2rVrW9xP63vf+x6jRo2iuLiYffv2ceedd3bq9URC0O0BwORIinBLRE8koUbELF26n0Q7VFRUsGvXLmbNmgXA9OnTOXjwICUlJU2Oe+6555g2bRqZmZkoisLChQt56RNjcGyczUFxcTHPPPNMsyrMgQMH2LVrF0uXLg3fl52d3bkXFQGa2wuAmpwa4ZaInkhCjYhZsp+laI+ysjJycnIwh5b2VxSF/Px8SktLmxxXWlpK3759w98XFBRwtNYPQLw1kXnz5vHII48QFxfX5Hl79+4lLy+PhQsXctFFFzFlyhQ+/PDDTr6qrqd5AoCEGhEZEmqEECLkq+sZtVbtO/04LRhACW3K8fiTT3HZZZdRVFTU7Dl+v593332Xm2++mV27dvGjH/2Ia6+9lkAg0HEXEAWC3iAAppT0CLdE9EQSaoQQAsjLy+Pw4cPhkKHrOmVlZeTn5zc5Lj8/v0mXVPH+z8hPNt5Kd+zYxRNPPEFBQQGXXHIJ1dXVFBQUUF1dTd++fcnNzeXrX/86AFdddRU+n6/V3dC7K82rAaCm9o5wS0RPJKFG9AiyTI04m4yMDEaNGsWWLVsAeP755ykoKGg24Hf69Om8+OKLHD9+HF3XWb9xIzOGxRHUFf7v/16htLSUkpIStm3bRmpqKiUlJaSmpjJ69GiSkpL4+OOPAfjggw8AyM3N7dLr7Gyaz/ivKS0zsg0RPZJsziFimAwUFu2zfv16br/9dlauXElSUhKbN28GYO7cuUydOpWpU6fSv39/li9fzsSJE9E0jXFjipgzOA43Vhym1j8nKorCE088wdy5c/F4PFitVp5//vlmY2+6M81Vi64ZnyDU9KwIt0b0RIreg6aIOJ1OkpOTqa2tJSlJphvGOlflScoumQhA/s5d2O22CLdIxKIvP36H/i98iwrSyLj/YKSbE1GBQ59RfNU0QGfIp5+imOVzs+gYbf37Ld1PokdQZP6T6CQ+txMAjyKhOVh1HAA1Dgk0IiIk1AghxHnwNxjbAnhVCTVadSjUxMuHCBEZEmpEzOo5HasikgIeYwNHn4QatOoqAExW+dMiIkPqg6JHkNlPorMEQ6HGb5ZdqYO1RqjxngxyYNyFmGxmTAnxmOxW1EQ7pmQHpqRkTCmpmNJ6oab1xpSehal3NqaMfJSkNNmnTZwXCTVCCHEeNI/R/RQwSaiJv3AMimkLelDBX6vjr/UDfqAeqDr7CVQdU7xR6TElxKEmxGOy2zA57JiSkjAlJ6OmpGJK623cemWh9srBlJmPmpjcyVcnugMJNSJm6aeXZ6QvSnQS3WeEmqBUarCO/yYD//Uq/i/2oFWVE6w6TrC6imBNNVptLUGnk6CrnqDLg+b2EmwIEPRoBL2AroCmEPRA0KNDjQ/wAS7gxFlfWzHpmCygWk2YEuIwJVgw2W2oDjum5CRMySmYUlJRU3th6pUVqhDlYMrIQ7HZO/knI7qKhBohhDgf3noAtPjECDckOpgy8jBl5LXrObqmodVUoFUcIVh51AhDVRVooUAUdNYSdNah1bkJuj0E641ApHl1gj5AV9CDCgE34NbgpBfwAs42vb5i1jFZFEyhQKTaLZjsCUaFKDkJNTkFU2o6plAgUtMyMWXkYOqVi2KVMBtNJNSI2HVapaYHLcckupjiN0KNHid/3M6VoqqY0rIwpWURx+h2PVcPBNBOHiN44gjBE8fQThqBKFhdRbC2Bq3WSdBZR7DeTbDeQ7Deh+YJEvToaH4ldA6FQAAC9UGoCgIeoLZNr6+adVSLgslmwmSLQ02wYEq0YUq0oyYnYUpKwZSWjik9A1OvTEwZfTBl5qH2ypXxQ53gvELNqlWr+OlPf8qdd97JmjVrAOOPx/Lly9mwYQPV1dWMGzeORx55hKFDh7Z6nhdeeIGVK1dy4MAB/H4/AwcO5Ec/+hG33npr+Jj777+f5cuXN3leZmYm5eXl53MJIpZJ95PoAmoo1CCVmohQzOZzqg4B6H4fwRNH0E4cJlh5zAhDJyvQqk8SrK0xusucLjSXm6DbS9DtJ9gQRPOdCkRaQEFrDES0IxApRndZ4/ghk92CyZGAyZGIKSU0mDq9N2qKUSFSUzNQ0zMxpWWhOFIlELXinEPNjh072LBhAyNGjGhy/+rVq3nooYd44oknGDRoECtWrODKK69k//79OByOFs+VlpbGvffey5AhQ4iPj+fll1/mjjvuICMjg6uuuip83NChQ3nttdfC35tMpnNtvugBpDojuoIacBv/tci4jO5GiYvHnNMPcvq1+7m612MEosojBKvKQ91llacqRHV1BJ0ugi43QVdDOBAFPTp6UAG9pfFDdcDxs794aEC1Gq9gsppRrWajQmQPVYiSHE3HEKVlGOOIeuWgZuahJrT8tzgWnFOocblczJw5k40bN7JixYrw/bqus2bNGu69915uuOEGADZv3kxmZiZPPfUUCxYsaPF8kyZNavL9nXfeyebNm9m2bVuTUGM2m8nKkv1ERNtop60iLJ9pRGcxh0KNYondPxSiOcVixdynEPoUtvu5mquWYHkJwYojBE8cNSpEVRUEq08SrK0lWFsXCkMetIYAQW8Qzauj+QFOH1ANfmcACNCeLrPGQdUmqwn1q4Oqk4xp92pKqjGG6PRZZhl9UOzJUV0lOqdQs3jxYq655hquuOKKJqHm4MGDlJeXM2XKlPB9FouFyy+/nO3bt7caak6n6zr//Oc/2b9/P7/61a+aPFZcXExOTg4Wi4Vx48axcuVK+vfv3+q5vF4vXq83/L3T2bZBYyL2qLK5pegk8UGj+8lsle4n0TZqYjLqgJHEDRjZrufpgYAxoPpkqDJUc8KoDtVWo9UaA6q1UJdZsN6NVu8l6PYR9ASMMUQ+gFODqgPnMqha1VEbu80aK0SJVkz2BNQkBwkTJpF44w/a+yPpMO0ONc888wy7du1ix44dzR5rHN+Smdl0y/nMzEwOHTp0xvPW1taSm5uL1+vFZDKxbt06rrzyyvDj48aN48knn2TQoEEcP36cFStWMGHCBPbs2UN6enqL51y1alWzcTii59BO635SZfU90UnitQYAzDap1IjOpZjNmHrlYOqVw7ns7d7ioOqTJwhWV6HVhMYQ1RlVIq3eYwSir84y0xSCDRBs0DHWIPJjTLs3VP3jUwZfeSNqakbHXHQ7tSvUlJWVceedd7J161asVmurxylf+QOi63qz+77K4XCwe/duXC4Xr7/+OkuXLqV///7hrqmrr746fOzw4cMZP348hYWFbN68maVLl7Z4znvuuafJY06nk7y89g8mE93T6bUZRSo1opNYQqEmztb6zsFCRIPzGlStaWgny9Eqj4Wn3YfHENVUE6g8wcnXPzfWG1Ii1z3VrlCzc+dOKioqGD361JS7YDDIW2+9xdq1a9m/fz9gVGyys7PDx1RUVDSr3nyVqqoMGDAAgKKiIvbt28eqVauajbdpZLfbGT58OMXFxa2e02KxYLFY2np5IuacNqU7gq0Qsc2qewCIj+HBl0Ioqnpalaj5tHv/F59w8vUbQdFRktIi0EJDu+LU5MmT+eSTT9i9e3f4NmbMGGbOnMnu3bvp378/WVlZvPrqq+Hn+Hw+3nzzTSZMmNCuhum63mQ8zFd5vV727dvXJDwJcbom1UFJNaKT2HSjUmNNkEqN6Lm0amPVZzWOiA4kblelxuFwMGzYsCb32e120tPTw/cvWbKElStXMnDgQAYOHMjKlStJSEjglltuCT9n9uzZ5ObmsmrVKsAY+zJmzBgKCwvx+Xy88sorPPnkkzz66KPh5/z4xz/m2muvJT8/n4qKClasWIHT6eS2224754sXse30UKPJ9G7RCXQtSIJifPiyJEqlRvRcWk0lYEwzj6QOX1F42bJlNDQ0sGjRovDie1u3bm2yRk1paSnqaUmuvr6eRYsWcfjwYWw2G0OGDGHLli3cdNNN4WMOHz7MzTffTGVlJb179+biiy/mvffeo2/fvh19CSJGnP5vTNe0CLZExCqP24Ut9HWCXTZUFD1XsMao1JgskV0/TtF70AplTqeT5ORkamtrSUqSUnGsq6mt59i4MQAUvPsetlT5oyM6VlV5KemPDUfTFfjZSVRT9K7fIURncm5awZFf/xlbroWC13d3/Pnb+PdbfgNFzFLU0/d+kkqN6HjeemNtDzdWCTQi4oqLi5kwYQKDBg1i7Nix7N27t8XjNm3axMCBAyksLGT+/PkEAoEmj+u6zuTJk+nVq1eT+xVFYcSIERQVFVFUVMTbb78dfkxzGgv/qdZzmWzeceS3UMSs03t2da3HFCRFF/KEQk2D0voSF0J0lQULFjB//nw+//xzli1bxpw5c5odc/DgQe677z62bdvGgQMHKC8vZ9OmTU2OWbt2LQUFBS2+xvbt28MThS699NLw/Vqd8btgskV2xrGEGhGzzrY2khDny9fQGGpsZzlSiM5VUVHBrl27mDVrFgDTp0/n4MGDlJSUNDnuueeeY9q0aWRmZqIoCgsXLuTpp58OP15cXMwzzzzD3Xff3a7XD7rqAFATIhvwJdSImKU26X6KYENEzPI3GCupelUJNSKyysrKyMnJwWw25v8oikJ+fj6lpaVNjistLW0ywaagoCB8jKZpzJs3j0ceeYS4uJa7kSZNmsTIkSNZunQp9fX14fs1l/G7oCYmdOh1tZeEGhGzlNNWtdRk9pPoBIEG49OpX0KNiAItreZ/tuNOP+Y3v/kNl112GUVFRS0+79ChQ3zwwQds376dEydO8JOf/CT8mOYK7VafGNk90CTUiJgla++Jzhb0hEKNSUKNiKy8vDwOHz4cHvSr6zplZWXk5+c3OS4/P79Jl9ShQ4fCx7z11ls88cQTFBQUcMkll1BdXU1BQQHV1dXh54KxPt2iRYuaDhR2GytrmyK8XpOEGhG7Tv80IpUa0QkaQ03AbI9wS0RPl5GRwahRo9iyZQsAzz//PAUFBc0G/E6fPp0XX3yR48ePo+s6jz32GDNmzADg5ZdfprS0lJKSErZt20ZqaiolJSWkpqZSXV2N221UYzRN49lnn2XUqFHh8wYbjEUoVUdkl0uRUCNiVtMp3VKrER1P9xljCiTUiGiwfv161q9fz6BBg3jwwQfDs5rmzp3LSy+9BED//v1Zvnw5EydOpLCwkIyMjBZnSX3VZ599xsUXX8zIkSMZPnw4VVVVrFmzJvy45vEDoCandvyFtUOHrygsRLRQFRkoLDqX4jUGR+pxEmpE5A0ePJh333232f2PP/54k+/nzZvHvHnzzniugoICKisrw9+PHz+ejz/+uNXjNY/R7WVKimyokUqNiFlNQ410P4mOp/iNSo0WL6FG9GyaNwiAmpIe0XZIqBExq8kIf8k0ohOofqNSQ3xkZ3wIEWlBn1EOV1N6neXIziWhRsSsJisKR6wVIpaZAsbAScUilRrRc+mahmYMqUFN7R3RtkioETFLUUALRRuZ/SQ6gzkUalRrZKexChFJuvMk6MZ7rSk9O6JtkVAjYpaiKOEKjS61GtEJ4oJGqDFbJNSInitYfdz4QtFRktIi2hYJNaJHkCndojNYNCPUmGwSakTPpVVXAKDGgaJGNlZIqBGxrXGwsOzSHbOKi4uZMGECgwYNYuzYsezdu7fZMSUlJUyaNInk5GTGjBnTYa9t0YxVVONtkV1wTIhI0qpPAKDGR34TYQk1okeQQk3sWrBgAfPnz+fzzz9n2bJlLS4klpSUxIoVK3jqqac69LWtulGpiUuQUCN6Lq26CgCTJfKRIvItEKIThcfUSKiJSRUVFezatYtZs2YBxhLwBw8ebLK3DUBaWhqXXHIJdnvHzlKy6UalxmaXUCN6rqDT2BtKtba8s3dXklAjYpreOPtJFqqJSWVlZeTk5GA2G4ujK4pCfn4+paWlnf7aetCPTfEBYLHLmBrRc2nOGkBCjRBdprLOG+kmiE5y+iKL0HWDwj31rvDXCYkpXfKaQkQjrc4JgMlmiXBLJNSIWBf6e+cLSqUmFuXl5XH48GECAWPfGV3XKSsrIz8/v9Nfu6G+FgC/bsJmtXX66wkRrYKhUKMmWCPcEgk1IsY1dj8lW2Xv1liUkZHBqFGj2LJlCwDPP/88BQUFFBQUdPpre1xGqHFjRTXJW6noubQ6o2qpJiZEuCUSakQPYZE/OjFr/fr1rF+/nkGDBvHggw+yadMmAObOnctLL70EgNfrpU+fPnz3u9/l448/pk+fPtxzzz3n9boet/HptEGRKo3o2bR6Y2NXU2Lkx5bJx1cR08IDhSPcDtF5Bg8ezLvvvtvs/scffzz8tcVi4fDhwx36uv5QqPEokS+5CxFJwXpjFqCaFPlZgPLxVcS20Jgamf0kOpq/oQ4Arxr5krsQkaQ1GBMxTMkpkW0IEmpETyEL1YgOFvAYocZnku4n0bMFG4ylDdTk1Ai3RLqfRIzw+IMcd3o4VuuhvNbD0doGyms9TAuvUxPhBoqYEwyHmo5d0E+I7kZrMGYfqinpEW6JhBrRDTT4gpQ7PRyraTBCi9PDsVBoORa6naz3tfjc60P/TZLZT6KDaR5jxkfQLN1PomcLejVAwZTaO9JNkVAjotNfPihj07aDlDs91Lj9bXqONU4lO9lGdrKVrGQr2clW4v+mQlBCjeh4uq8x1EilRvRsWugzpZqWEdmGIKFGRKlN2w7yWXld+HtbnInsFCs5ybZwYGn8b2OQSbbFNVtddv89KjJEWHQGxWtMY9XiJNSInkv3uNGDxvuuKS0rwq2RUCOilCNUWfl/11zAd8fkkWQ1NwssbaKEpz91YOuEAMVvhBo9XkKN6LmClcfCX6sSaoRoWbrd2EMk3qySbOuATdIk1IgOpvpDez/FJ0a2IUJEkHayHADVrKNYIr9mk0zpFlEpPTEegEpXywOA2+xcqjtCtIEp4AZAsUilRvRcwcZQY4mO91oJNSIqpScalZoq13nurq00TumWSo3oWHFBo/tJtUR+FVUhIkU7WQmAaomOOCHdTyIq9QpVaqrOt1LTSDKNOFfVJUb3pS0VrMnhoBwXbADAbJPuJ9FzBWtPAmCKkhmm0dEKIb6icUxNa+vPtJl0P4nz8fF/wwvzTn2vmMCWArY08v2HADDbIr+JnxCRotVWA6B2xNjHDhAd9SIhviI8pqa+Y7qfpFQj2u39jacFmsZZdEFwV0FVMRZ8aLqCktI3Yk0U4nTFxcVMmDCBQYMGMXbsWPbu3dvicZs2bWLgwIEUFhYyf/58AoFA+LHf/OY3DBs2jKKiIi6++GJ27NgBwN69eykqKgrfCgoKSEtLQ6szNnY12SI/SBikUiOiVMd3P0moEe3QUA2v/PjU91N+AV+bZ9wfui3b8gaf1yeyIr0gYs0U4nQLFixg/vz53H777Tz33HPMmTOn2Q72Bw8e5L777uPDDz8kIyOD6667jk2bNrFgwQI++ugjHn74Yfbs2UNiYiJbtmxh8eLFvP/++1x44YXs3r07fJ4f/OAHKIqC5jK6n9QES1deaqukUiOiTlDTCYZWzKtt8OMLnPvyedL5JM6J13Xq6yuWw6hbIc4KSdmQeSHF/gzWrv8z/7d+NTOvnXxOn4hLS0u59tprGTx4MEOGDOHhhx8OP/anP/2JkSNHMmzYMCZPnkxpaWmnXaqIDRUVFezatYtZs2YBMH36dA4ePEhJSUmT45577jmmTZtGZmYmiqKwcOFCnn766fDjfr+f+npjEHxNTQ19+vRp9lper5ennnqKOXPmoIWOVROiY7uQ8wo1q1atQlEUlixZEr5P13Xuv/9+cnJysNlsTJo0iT179pzxPC+88AJjxowhJSUFu91OUVERf/rTn5odt27dOvr164fVamX06NG8/fbb59N8ESENviAHKly89fkJnn6/lN9u3c/S/97NTevf5dLV/2TIfX/jqjVvhY+v87Rtm4QWyeJ7or1cFfDHbxlfW5PhkiXGOJrTLFiwgIQRV5E7fwM//M+lzJkzp9lpGj8Rb9u2jQMHDlBeXs6mTZsA431y2rRpzJ49m/3797Nv3z6++93vAvDZZ59x1113sXXrVj799FNmz57N97///c68YhEDysrKyMnJwWw2OmAURSE/P79ZIC4tLaVv31NdpgUFBeFjRo4cydKlS+nXrx99+vThv/7rv5qE7UYvvPAC/fr1o6ioiGC9MWBetUdHqDnn7qcdO3awYcMGRowY0eT+1atX89BDD/HEE08waNAgVqxYwZVXXsn+/ftxOFoeUJeWlsa9997LkCFDiI+P5+WXX+aOO+4gIyODq666CoBnn32WJUuWsG7dOiZOnMj69eu5+uqr2bt3L/n5+ed6GaITePxB9pfXcaSmgaM1DRypaeBIdQNHaxs4WtP65pOnM6kKWUlWLh3YizR7/Pk3SkKNaKviV6E29Icgpfl7S+Mn4uT5SwC46bvf5d6fLKWkpISCgoLwcad/IgZYuHAhq1evZsGCBbz++uvYbLZwkFEUhawsYzXWTz/9lKKiovDzvv3tb3PHHXdQVVVFenrkd0EW0eurq663tpTF6cedfsyhQ4d46aWX+OKLL8jOzmbt2rXMnDmTN954o8nz//CHP4SDvOZuDDXRMQvwnEKNy+Vi5syZbNy4kRUrVoTv13WdNWvWcO+993LDDTcAsHnzZjIzM3nqqadYsGBBi+ebNGlSk+/vvPNONm/ezLZt28Kh5qGHHmLOnDnMnTsXgDVr1vCPf/yDRx99lFWrVp3LZYhOMv3R7ew56jzjMYkWM7kpNnJSrOSm2shJsZEbuuWk2MhMsmJSO6DzSGY/ifYKbVSJKR5mPtfs4bKyMjKzsvGqJgAS4s3hT8Snh5ozfSLeu3cvvXv3ZsaMGezfv5+CggJ++9vf0r9/f4qKiti5cycHDhxgwIABPPnkk+i6zqFDhyTUiFbl5eVx+PBhAoEAZrMZXdcpKytr9qE/Pz+/SZfUoUOHwsf85S9/YdiwYWRnZwNwxx138MMf/pBgMIjJZAofv337dv7yl78AoHmMyRxqK0WLrnZOoWbx4sVcc801XHHFFU1CzcGDBykvL2fKlCnh+ywWC5dffjnbt29vNdScTtd1/vnPf7J//35+9atfAeDz+di5cyd33313k2OnTJnC9u3bWz2X1+vF6z01e8bpPPMfWtExvjhh/FEYmpNEv152clNDYSXZFg4wHbL1QTvI4nuizXzGGAGGfxccLe9lo4dGayXEm1BVpd2fiP1+P6+99hrvvfceQ4cOZcOGDcyYMYP333+fAQMG8Oijj3LrrbcSDAb59re/TXJyMnFx0TFlVkSnjIwMRo0axZYtW7j99tt5/vnnKSgoaBK0wRhrc8kll/Czn/2MjIwMHnvsMWbMmAFA//79efLJJ3G5XCQmJvK///u/XHDBBeFAA/DHP/6RadOmkZKSAoDWYFTeVUd0LELZ7lDzzDPPsGvXrvA0r9OVlxvLJTeWTRtlZmZy6NChM563traW3NxcvF4vJpOJdevWceWVVwJQWVlJMBhs8byNr9mSVatWsXz58jZdl+g4aQnxHK31sHLacEbmpUS2MeExNZFthugGAl7YuRkOvGZ8H9fyGIG8vDyOHT1CmhYkId5yTp+I+/bty6hRoxg6dCgAs2bN4vvf/374E/ENN9wQrnaXl5ezcuVKCgsLO/iCRaxZv349t99+OytXriQpKYnNmzcDMHfuXKZOncrUqVPp378/y5cvZ+LEiWiaxje+8Y1wV9K0adPYsWMHY8aMwWKx4HA42LJlS/j8uq7zxBNP8Mc//jF8n+YxBr+bklK78Epb165QU1ZWxp133snWrVuxWlufk95Sv97Zdlh2OBzs3r0bl8vF66+/ztKlS+nfv3+Trqn2nveee+5h6dKl4e+dTid5eXlnbIc4fymhUFPt7qDp2OdDup9EW33xL/jbT059/5XBwY0yMjIYdOFwDu35FwWTpp7TJ+Krr76au+66iyNHjpCbm8vf//53hg0bFv5EfOzYMbKzswkGg9x1110sXryYhCiZXSKi1+DBg5tN4QZ4/PHHm3w/b9485s2b1+w4RVFYtWpVq0M6FEVpNptK8wUBULtjqNm5cycVFRWMHj06fF8wGOStt95i7dq17N+/HzA+WTT2yYExsO6rVZavUlWVAQMGAFBUVMS+fftYtWoVkyZNolevXphMpmZVmbOd12KxYLFEx9z5niTVbpTJoyLUhEmpRpyFvXfT7w9th6MfQs6oZocuuf/XzJ1zB9vf/W8+cSTxxsvG+IK2fiK22+2sW7eOa665Bl3XSUlJ4amnngqf/4477qC0tBSfz8e3vvUtVq5c2XnXLcR50HwaoKAmp0W6KUA7Q83kyZP55JNPmtx3xx13MGTIEO666y769+9PVlYWr776KqNGGW8EPp+PN998Mzw+pq10XQ+Ph4mPj2f06NG8+uqrTJs2LXzMq6++ynXXXdeu84rOl5JgzFaqrj+PqdgdRaZ0i7bqMxpmvQDvrIGDb8Ghd2DDJOh7CUz4AQy8ClRjFYzJFxdx4YLfU+02/o33zje6htr6iRjgqquuCk+E+Kq///3vHXNNQnSyYOizq5rSK7INCWlXqHE4HAwbNqzJfXa7nfT09PD9S5YsYeXKlQwcOJCBAweycuVKEhISuOWWW8LPmT17Nrm5ueES16pVqxgzZgyFhYX4fD5eeeUVnnzySR599NHwc5YuXcqtt97KmDFjGD9+PBs2bKC0tJSFCxee88WLzpEWCjU10VCpkd4n0R4DJhu3Yx/Bu4/Ap8/DoW3GLX0AXLwIRt5MTkoCby37OsPv3wrAfX/9lAmFvfjeJf3wuF0cLt6NJcFBbv9hqKcNshQiluh+H3rAeJNVU3uf5eiu0eHbJCxbtoyGhgYWLVpEdXU148aNY+vWrU3WqCktLUVVT637V19fz6JFizh8+DA2m40hQ4awZcsWbrrppvAxN910E1VVVTzwwAMcO3aMYcOG8corrzSZMimiQ2pCY/dTFFRqGkmlRrRH9ki4YQNM/jm8vx4+eAKqDsD/LYV//gJG347lojk4rGbqPAFe21fBa/squHZkDid+fxUX+j8F4MOEiYxa9kpkr0WITqLVnAh/raZkRLAlpyh6D5rr6nQ6SU5Opra2lqSk6Jh+FksafEGOOz1sfreEP75TwjUjsnnklosi2qbiSy8jcOIE/V54HuuFF0a0LaIb87rgwy3w70ehusS4TzHhHvBtPsieweytxtvojnuvwPLbApJwA3BEyST3559HqNFCdC7/57s5MPVmUHWGfLoXRe28nZfa+vdbNrQUZxXUdCpdXsprPRx3Nt68lDtPfV9e68HpCTR5XkNoVHxEyewn0REsiXDxQhg7Dz7/O7z3KJS8TULx/3BZ8f/w1/hCNgWuxhZ3JQl6Q7jb06PKjCURu7TaSgBMcXRqoGkPCTWiiY/Kanhu52HKnR4qnB7KnR5O1HnR2ljPs8WZyEq2kpVkZc4l/Tq3se3QgwqSojOpJhhyjXEr/wTeewz9k/+miC94OH4tPLi2yTgun4QaEcO0GiPUqPHR8+FRQo1o4mf/8ykfHa5tdr9JVeidaCEz2Uqmw0JWspXMpMabhawkKxlJVpKs5rOuSdSlZPE90VmyhsP1j6BccT/B3wzE1MI/Mr9JQo2IXVptNQBqfPQMhpdQI5qwmI1/nDeO6cOVF2aRFQot6YmWjtmLqatFU8ASsSmxN3u/sZn4t1YyOPAZ1SSRirElywjPDnY8toAB1/6E1NwBEW6oEB0rWFcDgGqRUCOiVGaysVL0oEwHV1545gUTuxXpfhKdaPhl18FlxppZqUDN/bmkYOyB9rXyZyjf+Bre//d5+EODELFAc9YAoHbxXn5nEh0je0TUyHQYKzAfd3oi3JIOEq7USKgRHS+oBVscr1WcPLHJ91lUUlXr6qpmCdEltLo6AFRr9KzcL5Ua0UR2ig2Ao7WxEWrCI/I1LbINETFna8lW7n77bnR0kuKTuCDtAh6e/DBxahy60rwi46mvhfTkCLRUiM6huYxuVtUWPaFGKjWiidwUo/vpaE1DhFvSQULlfj0YBdPLRcw4evAzntqxEb/mJ6AFOOk5yTtH3+Fg7UEAzP7mVRmvq6aLWylE59LqjfWYTPboGRAvoUY0kdNYqYmRUKOYjGKkHgic5Ugh2s695RbKnJ82u/+o6ygAcYEWQk1981mFQnRnmtsINWoU7SAvoUY00RhqKuq8+ALdv8tGMUn3k+h4A4JfMKXeTVIwiHLamJp0azoA/rjEZs8JuCXUiNiiuY0Pv2pi83/vkSKhRjSRbo8n3qyi6zEyWDhcqZHup56ouLiYCRMmMGjQIMaOHcvevXtbPG7Tpk0MHDiQwsJC5s+fT+C0yt7LL7/MkCFDGDBgANOnT8flcuHR4/hBeTX+n32Ocvc+Anftg986ia82NnMt/N4f2FG0kvcG/ih8nkCDs3MvVoguFmzwAhJqRBRTFIXcULXmSAx0QSmNOyQHpfupJ1qwYAHz58/n888/Z9myZcyZM6fZMQcPHuS+++5j27ZtHDhwgPLycjZt2gSAy+Vizpw5/PWvf+XAgQNkZ2ez4hcPYFX82OJg8yvv8ccfX8O+hXbm5Bzn+zdcRmVFOclpvfna9Yu5eObP+MxaBIDmkVAjYovW4ANAdUTPAHgJNaKZnFgaLGySgcI9VUVFBbt27WLWrFkATJ8+nYMHD1JSUtLkuOeee45p06aRmZmJoigsXLiQp59+GoC//e1vjBkzhiFDhgCwaNEinn76GQBURaHvgCGMWvpXdgy9l2qPSpp2Ev+6iXy4fWv4/H6zHYCgp66zL1mILqWF9vtTk1Ii25DTyJRu0UxOcuwMFlYk1PRYZWVl5OTkYDYbb3OKopCfn09paSkFBQXh40pLS+nbt2/4+4KCAkpLS1t97OixY2i6DQ0TVksCV06ZwieffEJKUhZPX6eSzQl6/WMGr3/2Ay6b/fNT42u8EmpEbNF80RdqpFIjmskJdz91/zE1p7qfJNT0RF/dh6y1jU1PP+6rx7S2l5kbK6pJ5bXXXuPYsWPMmn0H62sv55OUrxOnBJlc+jt2rf4WAb/fOI9PQo2ILZrXmIChJqVFuCWnSKVGNJMd2iqhvLb7V2rC3U8yULjHycvL4/DhwwQCAcxmM7quU1ZWRn5+fpPj8vPzm3RJHTp0KHxMfn4+//znP8OPlZSUkJnRC1Vx04CVpND9qqoyb948Bg4cyHqnkz3/818M3P1LxvneA1/oGKnUiBijGXkdU2qvyDbkNFKpEc1kJBmrQ55weSPckvMXrtRoEmp6moyMDEaNGsWWLVsAeP755ykoKGjS9QTGWJsXX3yR48ePo+s6jz32GDNmzADgm9/8Jjt27OCzzz4DYN26dXxr8qUAlNXHcfLkyfB5nnnmGUaMGAGKwtDrl1J98yscU7PDj1vdxzrzcoXoUnogEA41akrvyDbmNBJqRDO9E41KzYm67h9qwisKS6WmR1q/fj3r169n0KBBPPjgg+FZTXPnzuWll14CoH///ixfvpyJEydSWFhIRkZGeJaUw+Hg8ccf5/rrr2fAgAEcOXKE7910DQClLhNXXHEFI0aMYPjw4bzxxhvhAAWQOXgc9h9uC39/kfvU10J0d5qzEjC6ZtW06Nn8WLqfRDO9Q5taVrp8aJqOqrY8pqA7CK8oLFO6e6TBgwfz7rvvNrv/8ccfb/L9vHnzmDdvXovnmDp1KlOnTg1/v/tvfwBgUJ90dj165qCSlNKL94ffz9hP7ued3jcx8YxHi47w5JNPUl1dTVVVFQ888ECkmxOztOoTxheKjmKXKd0iiqUnGguIBTWdarcvwq05T40rCgdlRWHRMYJeYwuEgMnWpuPHTv9PSm79N2Pnr+3MZomQXbt2ceedd1JXV9fqwHBx/hpDjRp32sbBUSB6WiKiRpxJJTUhDuj+42qkUiM6muZpDDVt3++moHAIcXHxndUkcZqLLrqI3/3udyQlJbU6c02cP622EgA1Prp+xtL9JFqUZo+n2u3nZH33rtTIlG7R0TRfPQDBuOjZxE+cMnv27Eg3oUfQaqsBMMVHV20kulojoka63RhX091DzakVhaX7SXSQUPeTFlopWIieSHMaoUa1RldtREKNaFGa3SiVV3fzUCN7P4kO53cDoMdLqBE9l1Zn7DqvWuMi3JKmJNSIFqWGQk1VNw81MqVbdDTVb3Q/IaFG9GDBOmODVtUaXWPFJNSIFqXHTKUmNFBYFt8THcQUMCo1SnxihFsiRORoLmOFbNVmjXBLmpJQI1rU2P20s7Q6wi05P0rjlG6p1IgOYg4YlRrVIpUa0XNp9aHfg4ToCjXRNcJHRI3GUPPpEWeEW3KewlO6JdT0dB21KJs5aOyJZrJKpUb0XFq9UbFU7dE1C1AqNaJFQ3OSwl97/N03EMjeT6JRRy3KFqcZocYsoUb0YJo7FO7t0fV7IKFGtKixUgMQ0Lrxqpyh7icZKCw6alE2SyjUxCUkneVIIWKX1mAszKomRleoke4n0SLHadP0gt041MiKwqJRRy3KZg2Fmnibo0POJ0R3pDUYk0jUpJTINuQrpFIjWmRWFRr3sezW3U/mxnVqZPE90TGseACw2CXUiJ4r6PUDoDqiq2IpoUa0SFUV0kKrCp+o68b7P6mNKwpLpaajFBcXM2HCBAYNGsTYsWPZu3dvi8dt2rSJgQMHUlhYyPz58wkETv0/ePnllxkyZAgDBgxg+vTpuFyu8GN/+tOfGDlyJMOGDWPy5MmUlpZ2+jW1la5pJOhGqLEmRM/OxEJ0Nc1rfNhVk1Ij3JKmJNSIVvUK7dZd2Y03tTxVqem+1aZos2DBAubPn8/nn3/OsmXLmDNnTrNjDh48yH333ce2bds4cOAA5eXlbNq0CQCXy8WcOXP461//yoEDB8jOzuaXv/wlAJ999hl33XUXW7du5dNPP2X27Nl8//vf79LrOxOvtwGzYlT9pFIjejLNa/weqClpEW5JUxJqRKt6O4xKTaWrGy/AJ3s/daiKigp27drFrFmzAJg+fToHDx6kpKSkyXHPPfcc06ZNIzMzE0VRWLhwIU8//TQAf/vb3xgzZgxDhgwBYNGiReHHPv30U4qKisjMzATg29/+Nn/729+oqqrqois8swbXqSUO7BJqRA+m+YyxlmpSrwi3pCkJNaJVvRIbQ003rtSEBgrL3k8do6ysjJycHMxm4+eqKAr5+fnNuohKS0vp27dv+PuCgoLwMS09duTIETRNo6ioiJ07d3LgwAHAWFtG13UOHTrU2ZfWJg31Rqjx6HGY46JreXghuoquaWiht1Q1VUKN6CbC3U/deEyNIlO6O9xXp0O3tubL6cd99ZjWplQPGDCARx99lFtvvZWxY8dSV1dHcnIycXHRsWme122EmgbFFuGWRLfOHnelKAojRoygqKiIoqIi3n777U6/JnGK7jwJuvE7bErNjHBrmpJQI1rV2P10ohtXamRF4Y6Vl5fH4cOHw398dF2nrKyM/Pz8Jsfl5+c36ZI6dOhQ+JivPlZSUkJubi6qarwd3XDDDbz77ru8//77zJ8/H4/HQ2FhYedeWBtJqGmbzhx31Wj79u3s3r2b3bt3c+mll3bJdQmDVl1hfKHoKEkypkZ0E9nJxhv30ZqGCLfk3MlA4Y6VkZHBqFGj2LJlCwDPP/88BQUFFBQUNDlu+vTpvPjiixw/fhxd13nssceYMWMGAN/85jfZsWMHn332GQDr1q0LPwZw7NgxAILBIHfddReLFy8mISE6lmL3u41qgVdCTas6e9yViLxg9QkAVDMoanTFiPNqzapVq1AUhSVLloTv03Wd+++/n5ycHGw2G5MmTWLPnj1nPM/GjRu59NJLSU1NJTU1lSuuuIL333+/yTH3338/iqI0uWVlZZ1P88VZ5KYab9xHqrtvqCH0CyeVmo6zfv161q9fz6BBg3jwwQfDn67nzp3LSy+9BED//v1Zvnw5EydOpLCwkIyMjPCndYfDweOPP87111/PgAEDOHLkCD/96U/D57/jjju48MILGTx4MMnJyaxcubLrL7IVAY+xM7FXlVDTms4ed9Vo0qRJjBw5kqVLl1If2lxRdA3NWQmAGn/uK3N3lnNeUXjHjh1s2LCBESNGNLl/9erVPPTQQzzxxBMMGjSIFStWcOWVV7J//34cjpZnC7zxxhvcfPPNTJgwAavVyurVq5kyZQp79uwhNzc3fNzQoUN57bXXwt+bGvf1EZ2iT4rxxl3u9OAPasSZoiuRt4WsKNzxBg8ezLvvvtvs/scff7zJ9/PmzWPevHktnmPq1KlMnTq1xcf+/ve/n38jO0gwEKD4wUvo7y+mTrHTRwcU8Jsk1JxJZ467glPdmfX19SxcuJCf/OQnrFu37jxaLNpDq60GQI2Pvr8J59Qil8vFzJkz2bhxI6mppxbe0XWdNWvWcO+993LDDTcwbNgwNm/ejNvt5qmnnmr1fH/+859ZtGgRRUVFDBkyhI0bN6JpGq+//nqT48xmM1lZWeFb7969z6X5oo16JVqIN6loOpTXeiLdnHMiKwqL87Hnnf9lSGAf8UqAdGrprdQC4E7MP8sze66uGHfVeJzdbmfRokUyULiLabUnAVCt0VdYOKdQs3jxYq655hquuOKKJvcfPHiQ8vJypkyZEr7PYrFw+eWXs3379jaf3+124/f7SUtrOgCpuLiYnJwc+vXrx4wZM/jyyy/PeB6v14vT6WxyE22nqgo5KVYADnfXLiip1IhztO/fWxnxr9sBqCaJL7+zlb3ffJZPvrGZkXPWRrZxXaw9s5kmTpyIz+fjiiuuIBAI8Pzzz5OXl8eCBQvo1asXvXoZU4Abx1199NFHTJkyhRtvvJHi4mJuvPFGxo4d2+q4q+rqatxuNwCapvHss88yatSoLvgpiEZanfG31GSJjlmJp2t399MzzzzDrl272LFjR7PHysvLAcILZzXKzMxs1zoTd999N7m5uU1C07hx43jyyScZNGgQx48fZ8WKFUyYMIE9e/aQnp7e4nlWrVrF8uXL2/y6orncVBslVW6OdNPBwo1TupEp3aKdnKUfh79OxUnqsHERbE1kNc5muv3223nuueeYM2dOsy7IxtlMH374IdXV1UyYMIHc3Fzy8vLYuHEjJ0+eZO3ateEKfOO4q+uvvx6v18uMGTN49NFH+elPf8ovf/nL8LirQCDA8OHD2bx5M2CsOr1gwQIURSEQCIR3XxddR3PWAKBau3moKSsr484772Tr1q1YrdZWj2upP/VM/aOnW716NU8//TRvvPFGk9e4+uqrw18PHz6c8ePHU1hYyObNm1m6dGmL57rnnnuaPOZ0OsnLy2tTO4ShT0oCUNV9Bws3riisSfeTEOeicTbT1q1bAaPC8oMf/ICSkpIms95On82UmZnJli1bWL16NW+88Ub4mMLCQsaMGRP+vqVxV+PGjeOxxx5rddzV+PHj+fjjj5vdL7qOFlozSE1oPQdESru6n3bu3ElFRQWjR4/GbDZjNpt58803+f3vf4/ZbA5XaBorNo0qKiqaVW9a8pvf/IaVK1eydevWZgOQv8putzN8+HCKi4tbPcZisZCUlNTkJtonPAOqxh3hlpwbJTQDg4B0P4n2CZa0vcs8lnXEbKa2CgaDPPLII1x77bXn33DRaYJuY7aZmhB9A+bbFWomT57MJ598El7waPfu3YwZM4aZM2eye/du+vfvT1ZWFq+++mr4OT6fjzfffJMJEyac8dy//vWv+cUvfsHf//73Jkm+NV6vl3379pGdnd2eSxDtlJvSGGq6aaVGpnSLczSh/tREhX+nXx+5hkSBjpjNdDa6rrNo0SJSUlL4j//4j/Y3UnQZzRUKNfboWD/qdO3qfnI4HAwbNqzJfXa7nfT09PD9S5YsYeXKlQwcOJCBAweycuVKEhISuOWWW8LPmT17Nrm5uaxatQowupzuu+8+nnrqKQoKCsKVnsTERBITEwH48Y9/zLXXXkt+fj4VFRWsWLECp9PJbbfddu5XL86qsVLTXQcKN07p9h06RNkPfgA6oGnQ+IarKMZNVUJvyIoRhBQFlNCbtKKGj1PU0DGnPY/QukmnjiN0n9ru8zc5rtXzK8RlZ5N0zbeibuGrWPKp7WsMazDGDibUFOPx+bHGR98Ygs52+mwms9l8TrOZ2uKHP/whZWVl/PWvfw3PchLRSXMbs2FVuz3CLWnunNepac2yZctoaGhg0aJFVFdXM27cOLZu3dpkjZrS0tIm/2jXrVuHz+fjO9/5TpNz/fznP+f+++8H4PDhw9x8881UVlbSu3dvLr74Yt57770m5U7R8RorNcdqPGiajqpG32JLZ2Lubcy00OrqcL32+lmO7l7isrNIaENVszsrLi7mtttuo7KykpSUFJ544gkuvPDCZsdt2rSJBx98EE3TmDx5MuvWrcNsNvPJJ5+wePFiKioqiIuLY/z48Tz88MNYLJazvrav7+UE932ASdEZHtzDu68/Q7+hF5OVP7AzLjVqnb6K9O23337GVaQvueQSfvazn5GRkdFkFemz+eEPf8iBAwf461//Sny8bBQa7TRPKNQkRt9O9Yre3hphN+Z0OklOTqa2tlbG17SRP6gx+P/9DU2Hf/90MplJ0Tcw7Gxcb76J/+jRU5WOcDUkVCLXdaOCoxsVHF3XQWu8XwddCx1H+Ptmx2H8V9e0JseFz9/suFbOH64iNW+HHnpd17/eIHjyJH0eWYtj8uRI/Vi7xDe+8Q1mz54dnnXz29/+tsVZNxMnTuTDDz8kIyOD6667jmuuuYYFCxZQXFxMQ0MDI0aMIBgMcssttzBy5MgmKxifid/nJfjLXKyKP3zfronruOjKmR16ndFu//793H777VRVVZGUlMTmzZsZOnQoc+fObTKgd+PGjfzqV79C0zS+8Y1v8Oijj4Y3I73ooos4duwYFRUVZGdn8/Wvf50//elPvPPOO1xyySUMGTIkHDb79evHiy++GLHrFWdW+q0x1H9ZT87i60n+j1Vd8ppt/fvd4ZUaEZ10XWd3WQ07D1Uz/aI+pNrb9mkozqSSlWTlaK2Hw9UN3TLUJF5+eaSb0KFKbr6FhpMnY35G17nMugFYuHAhq1evZsGCBQwceKqqYjKZ+NrXvhZe+6Qt4uIt7Bz4A9JLXiY7cJhEGgh+uQ3oWaGmI1aR3rVrV4v3T5w4sd3jb0RkaR4j5KuO5Ai3pDnpuIxxbl+Ap98v5dsPb2Pauu2s+L99bHj7zIsWflWfVGMwWLcdLBxrGrtuY/wPQUfPuqmvr+fxxx9v98yai2fdz8D/9wEl6ZcBkFa5s72XIkRM0TzGbFI1KfUsR3Y9qdTEqOLjdWx57xAv7DpCnbfpdObSqvZNz85NtUFJN9/YMpY0DmvSYjvUQMfNuvH7/dx0001MmTKF66677pzaomYPh6p/UEPiOT1fiFgR9BlVYjVZQo3oRL6Axt/3lLPlvUO8f/Bk+P6C9ARmjutLpcvL+re+pKKuffs4nZrW3T3Xqok1SjjVxHao6ahZN36/nxtvvJHs7OzzW3nWaryBa0r07XcjRFfSfMbOrmpyr0g3pRkJNTHgcLWbp98v5dkdZVS6fACYVIUrLshg1sV9mVjYiyM1DVy6+l8AuH3tW7Olu0/rjjmN3U8xPqamI2bdBAIBZsyYQVpaGhs2bGjzyuYt0XXj560r0msvei5d09BC4+bV1OjbVFpCTTfh8Qd598sqzKpCakI8KQlxfH68ju898UGT4zIcFmaMzefmsXlkJxth5OPDNUxd+074mPu+3XxK7JmEKzUSaqLD6TO3Ytz69eu5/fbbWblyZXjWDdBk1k3jHkITJ04Mz7qZM2cOAM8++ywvvPACI0aMCG96OHHiRB555JF2t8VvNtbksGuuDro6Ibofvb4WdOM9SEKNOGePvvEFv3u99S0hAB6deRFXXJhJnKnpJ8k9R0/tTp6VZOXi/i1vANqaU1slNLRrHy/RSRrXCorxSg2c/6ybmTNnMnNmx8xUsjiM3xtLoK5DzidEd6RVnwh/rSZFX/eT1FG7ibrQaPMkq5kMh4X4rwSXsf3SuHp4drNAA9C/16lVH2sb/Pz534d46/MTBIJt+6PYWKlx+4LUuP1nOVp0tsZVknXZebxL2UxGZcwvnwVFD6ZVHwdAjdNP7a0XRSTUdBPpica6Mt8clsX7917B/hXf5OCqb/Ha0sv4/c2jeOjGka0+d2y/NNJD69I0+IPc++KnzP7D+zz8zwNtem1rnIleicaiWDKtO/LCbyTBtm3SWVxczIQJExg0aBBjx45l7969LR63adMmBg4cSGFhIfPnzydw2iagv/nNbxg2bBhFRUVcfPHF7NhhbB9w9OhRrrrqKgYPHsyIESO48cYbOXnyZIvn7+7coU38NFVWvBU9l1ZbBYAapTuGSKjpJtJCoeRkvTEQWAntBzQgw8HUkTnhtWRaoigK79z9DX5x/TBmXZzPsFxjNcb95W0vo8tg4ShiNmbf6G3ceXzBggXMnz+fzz//nGXLloXHm5zu4MGD3HfffWzbto0DBw5QXl7Opk2bAPjoo494+OGHee+999i9ezc/+MEPWLx4MWAsaHffffexf/9+Pv74Y/r27cvdd9/dQRcaXTwNxuw/zXT2LRaEiFXBmlCosURnfIjOVolmUhOMUFMVCjXtZY0zcevFfVlx/XAWTxoAwAmXt83P79Pdd+uOIYrZ+Iik+88eahpX5Z01axZgzBQ6ePBgkynQ0HRVXkVRWLhwIU8//XT4cb/fT329UamoqamhT58+AGRmZnLJJZeEjxs3bhxfftm+xR27C4/HCDW6ufutqi1ER9GcNQCo8dHX9QQyULjbaOx+OnmOoeZ0vR3GJ80TdW0PNacqNbJWTaQ1dj+1pVJzplV5T58afaZVeUeOHMnSpUvp168faWlpWCwW3nrrrWavFQwGeeSRR7j++uvP4+qiWF05AIE42TdO9FxaXS0AqjU644NUarqJr3Y/nY/TQ01bpwXLtO7ooZhCi7+1cUzN+a7Ke+jQIV566SW++OILDh8+zH/+5382m1Gk6zqLFi0iJSWF//iP/2hTu7qT0r3vccGhPwPQkHlRhFsjROSEKzW26BxbJqGmm2gc6FvnCeALnN9U3sZBvw3+IPuOtW1cTa50P0WPuLZXak5flRc4p1V5//KXvzBs2DCys7MBuOOOO3jrrbcIBk/NvvrhD39IWVkZzz77LKoaW28r5aWf0+vZqaRiLI0w/JoFEW6RaI/zHSj/2muvUVRUFL7l5ORw0UVGsO1JA+UbaS5jnSaTLTrHlsXWu08MS7LGYQqtT1LtPr9qjd1iZmReCgDffWw7/9hTftbn9EmTUBMtwt1PbRhTc/qqvMAZV+V98cUXOX78OLquN1mVt3///mzbtg1X6M3sf//3f7ngggswhSpGP/zhDzlw4AAvvvgi8fHR+entfAR9HhKUU121h95/OYKtEe11vgPlr7jiCnbv3h2+XXTRReFKZU8aKN+oMdSotugcWyahpptQVYXUBGOAaJXr/Lug/nj715hQmE69L8iCP+3kv179HO0MGyQ2Vmpq3H7qvW3r9hCdIzxQuI2zn9avX8/69esZNGgQDz74YPjNeu7cubz00ksATVblLSwsJCMjI/zmP23aNK655hrGjBnDyJEjWbt2bTgkvfPOOzz88MOUlJQwbtw4ioqKmDZtWkdfckTlDhjBB31uD3/vKtkVucaIdumogfKNjh49yj//+U9uvfVWoGcNlG+kuY1xlardfpYjIyM6R/qIFqXZ46l0+TpkXE2aPZ4nvzeWX76yjz++U8LvXi9m3zEnD91URKKl+T8LhzWOJKsZpyfAkZoGBmU6zrsN4ty0d0zN+a7KqygKq1atYtWqVc0emzhxYqtjdIqLi7ntttuorKwkJSWFJ554ggsvbL5Fx6ZNm3jwwQfRNI3Jkyezbt06zGYzLpeL6dOns3PnTgAqKyubPK+6upof/OAHvP/++5jNZq677joefPDB1n8Q52HM3N/x74eOMc75D1SL7NLdXXTEQPnTbd68mauvvpqMjIxmj8X8QPmQoNuo1qv21pcRiSSp1HQjjYOFq+rbPmvpTMwmlZ9fO5Rff2cE8SaVrXuPc8O6dyiprG/x+NzQWjgyAyqylLi2dz9F0vmW/ePi4li2bBmvvfZai+f/3ve+x6hRoyguLmbfvn3ceeednXo9mtf4vbAlJnfq64iOdb4D5U/3xz/+scV/x7E+UP50mtsDgJoYnbMAJdR0I/1C2x2892VVh573u2PyeHbBxWQ4LHx+3MXUtdt46/MTzY7LD42rOVQloSai2jGlO1I6ouxvsViYPHkyKSkpzc5/4MABdu3axdKlS8P3NQ5k7ixq0PiEakuUKmV30RED5Ru99dZbuN1urrrqqmavE8sD5b9K8xgfqlVHdP4exPZPP8ZcOzIHgJc/PobH37H7/ozKT+Xl/7iEUfkpOD0Bbv/j+2x464smn1gK0o1QJaEmsto7piYSzlT2P11by/5ftXfvXvLy8li4cCEXXXQRU6ZM4cMPP+zYi/gKSyjUxFmj881cNNcRA+Ub/eEPf+D2228PD5BvFOsD5b9KC+1DqDqis2IpoaYbubhfOjnJVuo8AV7++FiHnz8jycoz8y/mpjF5aDqsfOUz/vPZ3eEA1bgA31GZARVR4dlPbRxTEykdWfb/Kr/fz7vvvsvNN9/Mrl27+NGPfsS1117bZL+qjmbRjX/3ZquMqelOznegPEBdXR3PP/883/ve95qcuycMlP+qxlBjSkqNcEtaJgOFuxFVVZg1vi+r/76fx978ghtG5aKqytmf2A4Ws4kHpw/nwpwkHnh5L3/dfZQDJ1ysv3VMeAbU0VoJNZGkhPZ+IorH1Jxe9jebzedV9m9J3759yc3N5etf/zoAV111FT6fj8OHDzf7FN5RrLoHFIizSaWmOznfgfIADoeDurrma3qdaaB8rNJ8xodcNTk6Q41UarqZWRf3xWExc6DCxWv7jnfKayiKwm0TCtgyZxxp9ng+PeLkurXbwhUaWVU4wrrBmJqOLPu3ZPTo0SQlJfHxxx8D8MEHHwCQm5vbsRcSous6NowBkvESakQPpvmMEKcmp0e4JS2TUNPNJFnjuHW8MQZh3RtfdOqnhPGF6fzP4olckJ1EpcvHff+zB4Bqtx+3L3r/oMa67jCmBjqm7H/RRRcxfvx4qqur6dOnT3h9EEVReOKJJ5g7dy4jRoxg0aJFPP/888TFxXXKtXgDGgmhUGNJkFAjeq6g3/ivmtI7sg1phXQ/dUN3TOzHpm0H2V1Ww7tfVjGhsFenvVZeWgLPf388y577uMk4nqM1DQzIkDf3SOguY2o6ouy/a1frC92NGTOG999///wa2UZub4AkjFkfFnt0TmUVorNp7jrQjCEPamp0hhqp1HRDvR0WbhyTB8Cjb3zR6a+XEG/m4ZtHseybg2kc0+nyduzsK9F2jevURPOYmljT4HFjVow910yW6FxJVYjOpp08taWOVGpEh5p/WX+eer+Ut4sr2XO0lqE5nTu9TlEUFk0awOj8VD496mRYjnxajZjQlNJo736KJV73aYNE4yTUiJ5JqzbWL1PMOkpcdE5fl0pNN5WXlsA3h2YB8I89nTNguCXj+qcz55J+mE3yTydSusuYmljiqzdCjZd4MMlnQdEzabXGwq9q5wxd6xDyl6kbG5ZrVGcOn5TF8HoSpRvMfoo1/gYj1HiwRLglQkSOVnsSAFN89EYH+cjRjfUJLYZXVu3mSE0D+8udfFZex/7yOtLtFv7fNRd0+Do2IvLCY2ok1HQZv8cFgEe1EZ3rqArR+bS6WgAUCTWiMzSGmh0l1Ux88J/NHv/2yGwuyo/OBZLEuZNKTdcLeELdT4otwi0RInK0uhoATNbo7X+K3rglzmpARiIOq/EHzqwqDM50MHVkDnnhjSdb3m1bdHMmCTVdLRgKNX6TNcItESJytDonAGoUhxqp1HRjDmscry+9nJNuH/17JRJvNjLqsuc+ouzkYf7z2Y+4bmTHb6UgIksqNV1P8xrj1nxqQoRbIkTkaC4j3KuW6Jz5BFKp6fYykqwMyUoKBxqAC7JPTbf++EhtJJolOpGMqel6us8YUxMwS6gRPZdWb/weqAnRO2BeQk0MunnsqQ0BK5yeCLZEdAap1ESAz+jKDZpkTI3oubR6o2Kp2qL390BCTQyyxpm44oIMACpdvgi3RnQ4CTVdTm8MNVKpET2Y5g6FGnv0LkApY2piiK7rnKjzsv94Ha/tqwCg0uWNcKtER5NKTddTfcabuR4noUb0XFqDUflX7dH7eyChppuqchnhZcNbX+L1awQ1nf3H66ht8Dc5LqB13i7eIjIaQ42Mqek6asCo1GiyRYLowbQG40Oyak+McEtaJ6EmynkDQYqPu9h7zMneo04+K3dSfNxFVX3L3UqqAgXpdgZlOrgwJ4nZ4/t2cYtFZ5NKTddTA6FVu+Ml1IieS/MYH5pVR/QuQXleoWbVqlX89Kc/5c4772TNmjWA0QWyfPlyNmzYQHV1NePGjeORRx5h6NChrZ5n48aNPPnkk3z66acAjB49mpUrVzJ27Ngmx61bt45f//rXHDt2jKFDh7JmzRouvfTS87mEqFJd72PfMWc4wOw95uRAhavFaouiQGpCPCfrfXx9cG+mFuUwKNNBYe9ErHGmCLRedBUJNV3PHAo1iuzQLXqwoLcx1ETvhsbnHGp27NjBhg0bGDFiRJP7V69ezUMPPcQTTzzBoEGDWLFiBVdeeSX79+/H4XC0eK433niDm2++mQkTJmC1Wlm9ejVTpkxhz5495ObmAvDss8+yZMkS1q1bx8SJE1m/fj1XX301e/fuJT8/v8XzRqvjTg+9Ei34Ahqbtn3Jh6U17D3m5FhtyzOVUhLiuDA7iQuykxiS5WBIVhKFGXYS4qXQ1iPJhpZdzhxsAECVUCN6MM0bBEBNjLFKjcvlYubMmWzcuJEVK1aE79d1nTVr1nDvvfdyww03ALB582YyMzN56qmnWLBgQYvn+/Of/9zk+40bN/Lcc8/x+uuvM3v2bAAeeugh5syZw9y5cwFYs2YN//jHP3j00UdZtWrVuVxGl9lztJZf/X0/X1S4OFLTEL5/8dcLeeRfXzQ5tm96AhdmJ4VDzIU5SWQnW1EUWUBPGBRzqBIXDKLruvzb6AJx4VDT8gczIXoCzacBoCZH7/Y75xRqFi9ezDXXXMMVV1zRJNQcPHiQ8vJypkyZEr7PYrFw+eWXs3379lZDzVe53W78fj9paWkA+Hw+du7cyd13393kuClTprB9+/ZWz+P1evF6T83+cTqdbXr9jvbM+2W89fmJZvc3BpqC9AR+/d2RDMly4Iji5adFdAgPFAZjsHCc/JvpbPGaEWrM1ugdIClEZ9N8OqCgJveKdFNa1e51ap555hl27drVYnWkvLwcgMzMzCb3Z2Zmhh9ri7vvvpvc3FyuuOIKACorKwkGg+0+76pVq0hOTg7f8vLy2tyGjtS4P9PEAencc/WQJo+NzEvhT3PG8bWCNAk0ok1ODzXSBdU1GkNNnE1CjeiZdE1DC02uVZPSItuYM2hXpaasrIw777yTrVu3YrW2vrHbV8vh7SmRr169mqeffpo33nij2Wu097z33HMPS5cuDX/vdDojEmwyHMaS0knWOBZcXsj8y/rz7pdVpNstDM6ScrZop9MqMxJquoZVN8a7xdnk91X0TLqnHnTj762a2jvCrWldu0LNzp07qaioYPTo0eH7gsEgb731FmvXrmX//v2AUbHJzs4OH1NRUdGsytKS3/zmN6xcuZLXXnutyQDkXr16YTKZmlVlznZei8WCxRL5PSoykoxwVlFndIUpisKEwugt34nopphOzW6TUNM1bHoDKBAvoUb0UNrJivDXakqMhJrJkyfzySefNLnvjjvuYMiQIdx1113079+frKwsXn31VUaNGgUY42HefPNNfvWrX53x3L/+9a9ZsWIF//jHPxgzZkyTx+Lj4xk9ejSvvvoq06ZNC9//6quvct1117XnErrEoap69h1zcrTGQ7nTw56jxqaSx2UfJtEBFJPJmNOv67IAXxfQNQ0bxgcSqz16p7IK0Zm0WmNcqGLSUeKid5fudoUah8PBsGHDmtxnt9tJT08P379kyRJWrlzJwIEDGThwICtXriQhIYFbbrkl/JzZs2eTm5sbHpezevVq7rvvPp566ikKCgrCFZnExEQSE40+7KVLl3LrrbcyZswYxo8fz4YNGygtLWXhwoXnfvWd4ECFiyn/9SYtLeSbaJEp2KJjKGYzut8vlZou4PM1YFGMWR8WCTWih9JqqgBQo3zoZ4f/lV22bBkNDQ0sWrQovPje1q1bm6xRU1paiqqeGqO8bt06fD4f3/nOd5qc6+c//zn3338/ADfddBNVVVU88MADHDt2jGHDhvHKK6/Qt290rZhb4/ah6RBvUrnywkyykq1kJ1vJTrYxoTA90s0TsSIuDiTUdAmPy0ljJ3ZCgnQ/iZ5Jc1YDoMZH9xIS5x1q3njjjSbfK4rC/fffHw4jbXlOSUlJm15r0aJFLFq0qH0N7GK9Q4OCVRXW3jJK1hARnUIxm9EB3S+hprN53HUkAx49DqtMnxc9VDjUWKJ7xfp2T+kWZ5bhMAYFe/waLq/8wRGdo3GwsB7wn+VIcb687joA3ErrMz6FiHVaXQ0AapQPo5BQ08Fs8SYcof/pjbOdhOho4bVqgsHINqQH8IVCjRcJNaLn0uqMxWtVS3RXKyXUdILGLqgKp4Qa0UniZFPLruJvMEKNR7VFuCVCRI5W7wJAtUXvzCeQUNMpwqGmTqZwi86hNG5q2c3H1BQXFzNhwgQGDRrE2LFj2bt3b4vHbdq0iYEDB1JYWMj8+fMJhMLcJ598wmWXXcaQIUMYPnw48+fPb7I1Skfwe4w3c68ioUb0XJrLCPeqLborlhJqOkHjYnsnpPtJdJLG7qf2jqk53xAB8PLLLzNkyBAGDBjA9OnTcblcp9qlKIwYMYKioiKKiop4++23z9ieBQsWMH/+fD7//HOWLVvGnDlzmh1z8OBB7rvvPrZt28aBAwcoLy9n06ZNAFitVtauXctnn33G7t27qa2t5be//W27fiZnE/Qa1+c3RfebuRCdSat3AxJqeqTeiUalRkKN6CzhVYU1rV3PO98Q4XK5mDNnDn/96185cOAA2dnZ/PKXv2zy/O3bt7N79252797NpZde2mpbKioq2LVrF7NmzQJg+vTpHDx4sNlsyOeee45p06aRmZmJoigsXLiQp59+GoCBAweGVx83mUx87Wtf48svv2zXz+RsNE9jqEno0PMK0Z1o7lCosUf374GEmk5gNhnTuGWgsOg04UpN2wcKd0SI+Nvf/saYMWMYMsTYmHXRokXhx9qrrKyMnJwczKFrURSF/Px8SktLmxxXWlraZD2qgoKCZscA1NfX8/jjj3PttdeeU3tao4cqNQGTdD+JnktrMIZTmBLsEW7JmUmo6WCfHK5lw1vGJ0VfoH2fooVoq3ClJtj2MTUdESJaeuzIkSNop1WMJk2axMiRI1m6dCn19fVnvo4WNqk923EtHeP3+7npppuYMmVKh2+dovmMT6iaWUKN6Lm0BuNDupoY3TvVS6jpQC9/fJRr124Lf7/sm4Mj2BoRy8Lr1LRzSndHhIgzLSh56NAhPvjgA7Zv386JEyf4yU9+0uqxeXl5HD58ODxeR9d1ysrKyM/Pb3Jcfn5+k2rSoUOHmhzj9/u58cYbyc7O5ne/+12rr3fO/BJqhNA8PgDUxOheVVtCTQeo8/j50X9/xA+e+hCAkX2SeePHk+ibHt1lOtGNhRffa3ulpiNCxFcfKykpITc3N7ztSeNxdrudRYsWnXGgcEZGBqNGjWLLli0APP/88xQUFFBQUNDkuOnTp/Piiy9y/PhxdF3nscceY8aMGQAEAgFmzJhBWloaGzZs6JwVvP2hWYwSakQPpnmMSQmqIznCLTkzCTUd4AdPfcjzuw6jKvAf3xjAc9+fQEEvCTSi85zqfmp7paYjQsQ3v/lNduzYwWeffQYY+7Y1PlZdXY07NJhQ0zSeffZZRo0adcY2rV+/nvXr1zNo0CAefPDB8IDkuXPn8tJLLwHQv39/li9fzsSJEyksLCQjIyM8wPnZZ5/lhRde4IMPPmDUqFEUFRWxePHiNv9M2kIPhEJNnIQa0XNpPuO9Rk1KiWxDziK61zvuJuLNRja8ZGBvfjRFupxEFzA3Vmra1/20fv16br/9dlauXElSUhKbN28GjBAxdepUpk6d2iREaJrGN77xjXCIcDgcPP7441x//fUEAgGGDx8ePsdnn33GggULUBSFQCDARRdddNbuoMGDB/Puu+82u//xxx9v8v28efOYN29es+NmzpzJzJkz2/UzaC8lFGrUuOieyipEZ9K8xrg51ZES2YachYSaDvDjKYN5fd9x3vr8BP/+sopx/WU3btG5FFNo9lM7BgrD+YcIIBx+vmr8+PF8/PHH7WpPd6AGQ6EmXkKN6Lk0vw4oqMnR/fdNup86wOAsBzPGGmMJfvF/e9G0UwMrX/roKDc+9i5vF5+IVPNEDDqX7idxbkyNoSYuutfnEKIzaaF1PtUUCTU9wtIrB+GwmPn0iJPndx3G5Q3wo//+iB8+/SHvl5xk6X9/hNMjOyqLDnIO69SIc2MKGlNZzRYJNaJn0hvq0TVjEL6a0jvCrTkzCTUdpFeihcXfGADA6n/s59u/fzs8eDjNHs+JOi9rXi2OcCtFrDg1pbt77/3UHZi0xlAjA4VFzxSsrgh/rSZLqOkx7phYQF6ajRN1Xkqq3OQkW3l63sWsuakIgM3vlvBZuTOyjRSxwRT61Q3KAo+dzRwKNSap1IgeSqsxQo1i0lEs0T22TEJNB7KYTfziumEkWsx8e0Q2f7vzMsb1T+eyQb355tAsgprOz/5nT6sLngnRVuc6UFi0X7xuLDoWL5Ua0UNptdUAqHERbkgbyOynDjZpcAYf/3wKqtp0EbD7rr2QNz6v4P2DJ/mf3Ue5flRuhFooYoEMFO46cXpj95OsPSV6Js15EgA1vhMWt+xgUqnpBF8NNAC5KTZ+8HVjzM0vX9mHyyufsMV5OMd1akT7xevGAP94q3Q/iZ5Jq20MNaYIt+TsJNR0kRq3j33H6gA4UefliwpXhFskujPpfuoauq5jwajUSKgRPZVWVwOAaon+zp3ob2EM2FZcyY//8hHlTg8mVWHJ5IGM6BPd+2eI6KaEKjW0Y+8n0X6+oIYVo1ITJ6FG9FCay/hArlqjPzJEfwu7MY8/yK/+/hl/fKcEgP697PzXTUWMzEuJaLtEDAhXamT2U2fy+AIkK0aosdpkTI3ombS6xlATH+GWnJ2Emk7y6ZFaljy7mwOhbqZZF+fz029dQEK8/MjF+Ts1UFgqNZ3J0+CmsaYaJ7OfRA+l1TeGmuiezg0SajpcUNN57M0v+K9XPyeg6fR2WFj9nRF8fXBGpJsmYkhj95Puj65Q8+STT1JdXU1VVRUPPPBApJtz3nye+vDXimyTIHoord74PVAToj/UyEDhDlRa5ebG9e/y63/sJ6DpXD0si38suUwCjeh4jdskdMGU7uLiYiZMmMCgQYMYO3Yse/fubXZMSUkJkyZNYt68efzpT3+irq6uyXpMpaWlXHvttQwePJghQ4bw8MMPhx/705/+xMiRIxk2bBiTJ0+mtLS006+prXwNbgACqOEuPyF6Gs0d2v/MHv3BXkJNB/nHnnKu/t1b7DxUjcNi5qEbR7Ju5kWk2aO/D1J0P105+2nBggXMnz+fzz//nGXLljFnzpxmxyQlJbFixQoWL17M8ePHSUpKQlGMpQ10XWfatGnMnj2b/fv3s2/fPr773e8C8Nlnn3HXXXexdetWPv30U2bPns33v//9Tr+mtvJ6jVDjQ36PRc+lNTQAoCZIqOkxHnvzC+p9QQZmJPK3JZdyw0V9wm/qQnS0U7OfOrdSU1FRwa5du5g1axYA06dP5+DBg5SUlDQ5Li0tjUsuuYSpU6eSmZnJ8uXLw4+9/vrr2Gy2cJBRFIWsrCwAPv30U4qKisjMzATg29/+Nn/729+oqqrq1OtqK7/HCDVexRLhlggROVqDsayBmuiIcEvOTkJNB/nu6DwAjjs92OKif4Ei0c2Zuqb7qaysjJycHMyh7i5FUcjPz29XF9HevXvp3bs3M2bMYNSoUUybNo0vv/wSgKKiInbu3MmBAwcAY0yOruscOnSo4y/mHPhDlRq/IpUa0XNpHmOrEAk1PchNX8vjguwknJ4Aj287GOnmiBinhEJGV8x++mrFsb17l/n9fl577TXuu+8+PvzwQ66++mpmzJgBwIABA3j00Ue59dZbGTt2LHV1dSQnJxMXFx2bzAQl1AiB5jGWNVATkyLckrOTUNNBTKrCt0dkA/Dx4ZrINkbEvK6a/ZSXl8fhw4cJhBb503WdsrIy8vPz23yOvn37MmrUKIYOHQrArFmz2LlzJ8FQlemGG27g3Xff5f3332f+/Pl4PB4KCws7/mLOQdBnjCUIqNL9JHouzWv8rqpJ0b9orISaDpSTYkx3O1rjiXBLRMwLrVPT2d1PGRkZjBo1ii1btgDw/PPPU1BQQEFBQZvPcfXVV3PkyBGOHDkCwN///neGDRuGKXQNx44dAyAYDHLXXXexePFiEqJkQGJAQo0QaD7jfcbkSItwS85O5ih2oMYwc9zpQdd1GSgsOk149lMXbJOwfv16br/9dlauXElSUhKbN28GYO7cuUydOpWpU6fi9XopLCzE6/VSW1tLnz59uPXWW1m1ahV2u51169ZxzTXXoOs6KSkpPPXUU+Hz33HHHZSWluLz+fjWt77FypUrO/2a2koLh5roX59DiM6i+XVAQU2RUNOj9Eo0+t3dviDr3viCxaFduYXoaEpozIke8Hf6aw0ePJh333232f2PP/54+GuLxcLhw4dbPcdVV13FVVdd1eJjf//738+/kZ2kMdRoJhlTI3ouzRgnjJoU/aFGup860I1j8vjh5IEA/Pof+3nnQGWEWyRilRIfGkgbZSsKxxrd3xhqpFIjeibd40bXjF4HNaV3hFtzdhJqOpCiKMwe35fcFGOPmPlPfsDRmoYIt0rEosbZT7q/8yo1bVlJGGDTpk0MHDiQwsJC5s+fHx5U3EjXdSZPnkyvXr1afP73vvc9FEXB5XJ1+DWcL91vdClLqBE9lVZdEf5aTY3+1fEl1HSwXokWtv7nZTgsZup9QR569fNIN0nEoHD3UyeGmrasJHzw4EHuu+8+tm3bxoEDBygvL2fTpk1Njlm7dm2rA4v/93//N6rHnql+Y88bPU42sxQ9k1ZrLISpqDqKNToG8J+JhJpOYFIVGt+nc1LkzVB0gk6u1LR1JeHnnnuOadOmkZmZiaIoLFy4kKeffjr8eHFxMc888wx33313s9eoqqpi+fLlPPTQQ51yDR3B5DeqR8G46F90TIjOoNUawyjU6Fg66qzOK9SsWrUKRVFYsmRJ+D5d17n//vvJycnBZrMxadIk9uzZc8bz7Nmzh+nTp1NQUICiKKxZs6bZMffffz+KojS5NS61Hm3WvfEFTk+AnGQrCy/vH+nmiBjU2ZWatq4kXFpaSt++fcPfFxQUhI/RNI158+bxyCOPtLiY3uLFi7n//vtJTo7etS/MAaNSo8VH/6JjQnQGreYkAGp89FZUT3fOoWbHjh1s2LCBESNGNLl/9erVPPTQQ6xdu5YdO3aQlZXFlVdeSV1dXavncrvd9O/fnwcffPCMQWXo0KEcO3YsfPvkk0/OtfmdpqSynsfe/AKAn117IQnxMsFMdDylcZ0arfPWqWnrSsKnH3f6Mb/5zW+47LLLKCoqavacv/zlL8THx/Ptb3+7YxrbSeICRqVGt0ilRvRMwbrGUNM9tv85p1DjcrmYOXMmGzduJDU1NXy/ruusWbOGe++9lxtuuIFhw4axefNm3G53k3UpvuprX/sav/71r5kxYwYWS+uLXJnNZrKyssK33r2jayS2ruvc/7978AU0LhvUm6uGRmclScQANfSrq7Vvy4K2autKwvn5+U26pA4dOhQ+5q233uKJJ56goKCASy65hOrqagoKCqiuruZf//oX//znP5ss5Dd06NCo+6BiCdaHvkiMbEOEiBCtzgmAaonhULN48WKuueYarrjiiib3Hzx4kPLycqZMmRK+z2KxcPnll7N9+/bzaylG/3xOTg79+vVjxowZ4U3xosXr+yp4Y/8J4k0qy6cOjeoBkKJ7U8KhRuuU87d1JeHp06fz4osvcvz4cXRd57HHHgvv6/Tyyy9TWlpKSUkJ27ZtIzU1lZKSElJTU1m3bh2HDx+mpKQkHIr27NnD8OHDO+V6zlVjqFGs0v0keiatrhYA1do9BtW0u2/kmWeeYdeuXezYsaPZY+Xl5QBkZmY2uT8zM/O8d90dN24cTz75JIMGDeL48eOsWLGCCRMmsGfPHtLT01t8jtfrxev1hr93Op3n1Yaz+fiI8T8/yWbG3k1KdaKbUhu7nzon1EDbVhLu378/y5cvZ+LEiWiaxje+8Y0WZ0l1V1bNCDUmW/SO+xGiM2kuY+iIau0eC1C2K9SUlZVx5513snXrVqzW1tdtaKkv/nyrFldffXX46+HDhzN+/HgKCwvZvHkzS5cubfE5q1atYvny5ef1uu0x55J+/N/HR/niRD3z/rSTZ+dfjDVOwo3oeIoa+n3qxFDTlpWEAebNm8e8efPOeK6CggIqK1tfjLK9O393lYTGUCOVGtFDhUONrXvsf9au7qedO3dSUVHB6NGjMZvNmM1m3nzzTX7/+99jNpvDFZrGik2jioqKZtWb82W32xk+fDjFxcWtHnPPPfdQW1sbvpWVlXVoG74q2RbHptu+RkpCHB+V1XDThvfY/oWsKiw6QRcMFBZgxhhTFHhnbYRbIkRkaG43AKqteyxA2a5QM3nyZD755BN2794dvo0ZM4aZM2eye/du+vfvT1ZWFq+++mr4OT6fjzfffJMJEyZ0aMO9Xi/79u0jOzu71WMsFgtJSUlNbp2toJedx2aNxhZn4qOyGm7Z+G9u2fgex52yc7foQErnDhQWhi+LlgEwyvk67z/7YIRbI0TX0+pDoSahe6y51q5Q43A4GDZsWJOb3W4nPT2dYcOGhdesWblyJS+++CKffvopt99+OwkJCdxyyy3h88yePZt77rkn/L3P5wuHJJ/Px5EjR9i9ezcHDhwIH/PjH/+YN998k4MHD/Lvf/+b73znOzidTm677bYO+DF0rIv7p/PmTyYxe7yxfsf2L6p45v3OrRKJnkUxhX51g1Kp6UxjrlvM9v4/BKBo76859NnOCLdIiK6luY0P5KrdHuGWtE2Hryi8bNkylixZwqJFixgzZgxHjhxh69atOByn1nkoLS3l2LFj4e+PHj3KqFGjGDVqFMeOHeM3v/kNo0aNYu7cueFjDh8+zM0338zgwYO54YYbiI+P57333muy8Fc0yUiy8rUCY0fTeJPKNSNarygJ0W6h2U/ROhYlllw8czkfWscSrwRwPr+kUwdnCxFtNE9jqOkeyxooeg96V3Q6nSQnJ1NbW9vpXVE1bh9XPPQmlS4fS68cFN69W4iO4C0u5strpxKIt3Dkprk4+vclc1A/cgb3JyEx+vdn6W4qyz4n8fEJWBU/u8f+lqJvzT37k4SIAaXXjKH+i3qyF15LypLVEWtHW/9+y3K3nWTVK59R6fIxMCORhZcXRro5IsZU6Mb0SrPPS98/PQKAHzgENJjj8cZZ8cZZ8VusBCw2ghYbWoIdJS+ftIuKSMxMJ85qITmzF6k5GRKEzqJX3iC259/BhLINOHauBQk1oofQPMZgedXRPWYASqjpBO9+UcWzHxhjaFbdMJx4s+wbKjqWNyWNX4y9jZEnv+RCpZ6EquOkOiuxBbzYAj5sAR80tLAu0yfvwCunNpx0hm4eUzz1VjsNtkR89iT0uHiCOX2I79MHc0oycSnJ2NJSSclKx94rjcT0VBx2Kya15ywwOeiaH8JjGygMHuTAztcYMPqKsz9JiG5O8zaGmpTINqSNJNR0sEBQ494XjaXeZ47LZ0xoXI0QHUnXYXvOcPYNuIgPf2as4K1pGtXHKnFWVuOudeKprcNT68JXV4e/zoW/ppbgFwdwlHxOkrsWnymORG89Zl3DGvRhrfdBfTU0rkJw4MNmr6sBdaFbsdlKfXwC3ngr/ngrAYuVoNWGZrWBLQESElATEjDZEzEnJhDnSMSSmIgl2YE12YE92UFCShKOVAd2hx2zKbrDf6+sPHbaL2N0/Vvk/O9MjihPkHvR1Wd/ohDdmOY1JiOoyd3jb5mEmg5W2+Cn9KQxBW7PUSdlJ93kpUlpX3SslgbCqapKem4G6bkZbT6Ppmk4q2qoOVaJs6KSuopKqj/ag/bFAVBVFL8Pk7ue+AYXFreLBG89CX5j4GBiwENiwAPuc7uGIKcCUlBR8Zjj8cRZ8cdZ8VmsBC02glYbuu1USDIl2DHZE4hzOIhzJBDvcGBNSsSa5MCWlIg9NQl7ahKWRDuqqeMXvhy44Ek++v11jAx8RMNL3yc48ktMUR7GhDgfml8HFKnU9FTpiRYemXkRP/nLR+wuq+Fbv3+bX00fwbeGy+wn0fHOd6VuVVVJ6Z1GSu80YJBx5y1Tz/gc3e+nobqGuhPVuCurcTudeGtdeOtc+Opc+F0ugvVutPp6dLcbGtwoDQ2YPG5MXg9xXg/x/gYsfi/WgA8Ak65h93uw+89tPScdI1u5gROh+zzmeLxmC954K4F4K/5QSNKsNnRbAorNBqGQZEq0E2e3E+dIJD7JgdWRiC3ZCEsJKUnYUxxYbRaSklLJX/xX+F0/0qmm5HApBX0LzqnNQnQHmt/4r5rS8nZE0UZCTSe4amgWQ3OS+OHTH7KrtIZFf97FzHH53PftC2XbBNEhGucsRmJEixIXR0JGbxIyep/3ufRgEI+rnrqTtbhrXNQ3dps56/DW1eOvcxFw1ROsr0errwe3GzwNqA1uVE8DZq+HOL8Hi8+Dxe/FFvBi0o0p19aAzwhNnrpzalsQqA/dTgB+1USD2Yo3zkKmOQuLOYC+7VoO222otnhUmw01wYZqt6PaE1ETHaiOZNSkVNTkVEwp6aipvVBTeqOm9EKJt5/abV2IKKR7PehB411GTWl7BTiSJNR0kj6pCTy7YDz/9ernPPrmF/z536V8UFLN47eNke4oIUIUkwlbchK25I6ZWREIatTX1VNfU4e71om71oWntg5fnQuv00XAdVpIcruNkNQQCkneBszeBuK8HuJ8Hix+IyhZNGOgZJwWJM5XD756NFQaiAcCeDiX0KSjxumocWCyqKhWE6o1DpPNgmq3otoTMCUmojqSUB1JmFJSUZPTUFN6Y0rNQE3PRO2Vg5qQBOdZrROiNVrN8fDXplQJNT1enEll2TeHML4wne9v2cX+43Vs2naQ+6cOjXTTRDenh0bVyN+zpswmleQUB8kpDiCnQ87p93hpqKunvsboZmuoq8NXfZL0k5/h8LvQXE40V51xc7uNW4MHrcGL5vET9PjRvBqaTyPoAzQFUND8CpofAm4w6kJBwAPUtrltiqqja6f+EST0tWFKsKDabZgS7agOByaHI1QpSkVN6YUprTdqeiam9GzUtCyUuO6x+7Loelq1MWtAUXUUW/dYUVhCTRdITYjH7TM+7U0Z2rEbewohOlec1UKc1UJS76/O/rim3efSdR3d40GrOUHw5Am06hNo1RUEa6rQak+iOWsIOp1odXVo9fUE6xuMgOT2EfQE0LxBNJ+O5jeCzOmBBsB9qAFoAGra3TZLmoLJZkZNiEeJi0O1xIe700yOUNUoKdkISMlpqKm9UVN7Y0rPQk3NQonvHrs4i7bTao1Qo8ZFuCHtIKGmk2mazs/+51M0Ha4dmcOEwl6RbpKIAafWAZdSTXeiKAqKzYZqy8ecnX/O59EDAbTqCrTqcjyffozviy8w2Uwoip9gbQ2asxatro6gKxSM3F6CDT60hgBBr4bmIzxWopH3pI6xhKP/3K7NrGOyKJisKqrVjCnBgsluRXUYXWmmZAem5FTUlDRMqb0x9cpE7ZWNqXcfY7yGqRv95ewhNGc1AGpc93mfkVDTyZ7bdZhdpTXY403c+60LIt0cESPCA4W7z3uN6ECK2Yypdw6m3jnEDbronM6huevQKo/h++gdNLcLxZJA8GQlweoqdF8DuttF0OVCc9Wj1TcQdJ/qUtM8QSMc+U+FIz2gEAhAoF4DfKFb28YbKaqOatExWVSjWmSLw2S3Ykq0Y0pKDI0rSjEGW6f1wpSeialXNqbeuShp2Shm6ULrDMHaKgBUS/eZ4CKhppM9+sYXANx5xUCykq0Rbo0QQhjUBAdqvgNz/qDzOo/mdqGdPIZWVU6w6rjRrVZTRbDmJMGaWjRnLcG6eoIudygc+Qg2+NE8GkGfDrqCrikEGxSCDUBNAAhgdKNVn70Bio4pXg9ViEyYEuIx2S3GYGtHIqakJNTkFEyp6ZjSehtjiuI0THkXoPa5EMUsFaLWaE5jfJdq7T5Rofu0tJtyNhil3MsHdY+R46J7CA8UjnA7hFATElETBkKf9m/aq2ua0VVWeYzgiSMEq8oJnjyOdjIUipy1aM5QN5qrgaDbi9bgJ+jRCHp1Y9C1rhD0KgS9QG0QIwy1cVyREpqBZlUwWc2oCaEKkT0B1WE3ZqAlp2BKSkZNSQ9VijIwpRldZ2pKBoo5dv+Mas4aQEKNOE3j3jhBrcdshi6EEG2iqCqmFGNmVtyAC9v1XF3X0V11BCuPEjxxxKgUnawgWF2JVl1NsLaGoLMOzeUKB6Kg228MuvarRreZrqD5QPOB39nOCpHRCtR4MFkUVIsJky0ONSEeU4ItXClSHQ5MySlGOErphZqabgyuTssyglGCo50/ta6j1Rn7x6m27jMIXEJNJ5NQIzqDjKkRPZ2iKP+/vXuPjarM+wD+PWc6nV7odCm9bWlBEBZRZFeKsqzrlhUtEG4RE0EQzftiEBUXYnbXRZL1FmGDrIsJuxvRAOu6vrgxouSFiFRQQXx9a22xVC5vSUOB3u9XZjrn/N4/ZubQYTq0nc7ptNPvJ5m0c+acZ57n1zb95jnPOQPFcx8f64Rb+n283toIvb762gxRfQ30xjpoTY3QWpqgt7ZB86wnMhZae9YS+Vya7wlFaO1+WX4PHyYbaBwWbzBSocZGwRIbDTXO5g5GYZ4t0tvca6IsccNn6QRDjcm8ocal62HuCUUihSegiIKiJoyGmjAaUTf1PxCJrkPaPafN6qvcl+U31EJvboDW1AC9tcV9aX6bZy1Rx9VrV59dve6yfM29lkjrFKDJe/VZe3964w5F0Yp7TZExWxTjvrt1Qjwsnsvx3fcqcl99po52L7a+0WyR3u7uhxo3fG4Yy1BjsijO1BARRRRFVaF4Q9GE/p0285IuJ/TGamh1FdAbaqA11BgLrPWWZvd6Iu9sUUcn9HaHZ4F1t8vyr58tagt+tshiAyxxUbDER8MyKhYW+yhcvVABAFDjGWoI7nO+dW3uD+xLiOEKewodnn4iGt4UazQsqVmwpGYF3Ybe1gy99gq0xmr3qbQBzBa5OgBXhwbUeRdaNxjvYxk9PD7MEmCoMdXlxk60OVywWhRMTBket5gmIqLhQR2VCHVUYkhmi7SaCmg1V6DVVUOrr4XW0ACtuRmwWGD/z9+FuOfmYagx0bkq9yKrm1NGwWrhp/FS6PCSbiIaKJ/ZouBy0ZDD/7QmOlftDjW3pA/dS/ZoeFN4/omIyMCZGhP8X3Ur3j5ehv1FVwAAU9LtYe4RRRrhunMiIj8MNSG26cNi/Nf/lhvPZ4z7ER64Y2wYe0RERDQyMNSEULvDZQSaebelYe2vJiJ7fFKYe0WRiBM1RET+GGpCqKzOfaOiMfHReHP1zDD3hiKZeM4/cUkNEdE1XCgcQhdq2wC4r3YiMpN3pkZlqiEiMjDUhNCFWvdMzc2pvCcNmcs7U6My0xARGRhqQsg7UzMxmTM1ZC7N81FinKkhIrqGoSaELjd2AgBOXW6Cw6WFuTcUyXSuqSEi8sNQE0Ir78qCqgD//X0lVuz6H7Q5XOHuEkUob6ix8PwTEZGBoSaElt85Dnv/4y7YY6JQWN6E3SfKwt0lilDem+/x9BMR0TUMNSH2q5+k4HfzbwEAHCquNBZ0EoWSpntPPzHUEBF58T41JrjouV/N2apWTNh0CKoCRKkqVNX91aIqxiNKVaAqCqIsnm2KZ7ul2/c3Otbz1Xusbzsqoiye9q/f97rv/fuidnv/wO/p7VOUxb0tSlVhMb73f25RlR7/ETd1OFHd4sBP0kbxH3Uf6Lz6iYjID0ONCZITbD7PdQGcmg5oAKCHpU9DiUX1DTlRFhUN7U4A7hsXptpjEBdtgS1KRXSUimiLCpvVgmiL+7nN84ju9rWnfaKNfSw+x3i3eY+zWnoOWkOZdwKQa2qIiK5hqDHBupyb8ejs8ehyCVy6Dk0Emi5waQJdBC7d/dz7cPl8r0PX4T7uun10cbeh6QLN246mQxNA03X3Pnpv7Xffp9uxPfStp3Z0Tx+NPmjXXnNp7u1d3Y7pifc1Rw+v1bc7Ue8JOINFUdAtDFkCBCbVZ59AwSnQvtHXBTHv/jFWC2Ks7q9RAWaxesLTT0RE/hhqTBIXHQVEh7sX4SXdQpI39Hifd3kCkMsT9ly6jgSbFXXtDrR0duFqlwaHS4fTpRtfnZoOR5cOp6b5bvd87zD20eDU9B72uXacq1vgEoFxfCvCd8WaqsATciyIiXLPPHmDz/UBqK7NYRxDRERuDDVkGkVRYLUosFr6fsy4MXHmdagbXRcjJDkChCR3kNI8QapbcLouJPkFr27hyTeYadfe06Ubwc3okwAdTg0dzr7f48geYzWjPEREwxJDDY1IqqogRnXPigDhCwYi4g4/XTquujQj6Fzt0nC1y/u12zaXeybK4XKfMlw4/cdh6zsR0VDDUEMURoqiGKecEsMYroiIIsGA7lOzdetWKIqCjRs3GttEBC+++CIyMjIQGxuLOXPmoKSk5IbtlJSU4MEHH8RNN90ERVGwY8eOHvf729/+hgkTJiAmJgbZ2dk4fvz4QLpPREREESToUJOfn49du3Zh+vTpPtu3bduG119/HTt37kR+fj7S09Nx//33o7W1NWBbHR0dmDhxIv70pz8hPT29x33ef/99bNy4EZs3b0ZhYSHuueceLFiwAOXl5cEOgYiIiCJIUKGmra0Nq1atwltvvYXRo0cb20UEO3bswObNm7Fs2TJMmzYN//jHP9DR0YH33nsvYHt33nknXnvtNaxYsQI2m63HfV5//XWsWbMGjz/+OKZOnYodO3YgKysLf//734MZAhEREUWYoELN008/jYULF+K+++7z2V5WVoaqqirk5uYa22w2G3JycnDy5MmgO+l0OlFQUODTLgDk5ubesF2Hw4GWlhafBxEREUWmfi8U3rdvH7777jvk5+f7vVZVVQUASEtL89melpaGixcvBtlFoK6uDpqm9diu9z17snXrVrz00ktBvy8RERENH/2aqbl06RI2bNiAd999FzExMQH3u/4upyISkjuf9rfdTZs2obm52XhcunRpwH0gIiKioalfMzUFBQWoqalBdna2sU3TNHz55ZfYuXMnzp07B8A9Y/PjH1+7f0ZNTY3fLEt/JCcnw2Kx+M3K9NauzWYLuEaHiIiIIku/Zmrmzp2L4uJiFBUVGY+ZM2di1apVKCoqwsSJE5Geno4jR44YxzidTnzxxRf4xS9+EXQno6OjkZ2d7dMuABw5cmRA7RIREVHk6NdMTUJCAqZNm+azLT4+HmPGjDG2b9y4EVu2bMHkyZMxefJkbNmyBXFxcVi5cqVxzKOPPoqxY8di69atANzB54cffjC+v3LlCoqKijBq1ChMmjQJAPDss89i9erVmDlzJmbPno1du3ahvLwc69atC370REREFDFCfkfh3//+9+js7MRTTz2FxsZGzJo1C59++ikSEhKMfcrLy6Gq1yaJKioqcMcddxjPt2/fju3btyMnJweff/45AGD58uWor6/Hyy+/jMrKSkybNg2HDh3C+PHjQz0EIiIiGoYUEZHed4sMLS0tSExMRHNzM+x2e7i7Q0RERH3Q1//fA/qYBCIiIqKhgqGGiIiIIsKI+pRu75k23lmYiIho+PD+3+5txcyICjXeD9XMysoKc0+IiIiov1pbW5GYmBjw9RG1UFjXdVRUVCAhISEkdzgG3OkxKysLly5d4uJjD9bEH2vijzXxx5r4Y038jcSaiAhaW1uRkZHhc/X09UbUTI2qqsjMzDSlbbvdPmJ+ufqKNfHHmvhjTfyxJv5YE38jrSY3mqHx4kJhIiIiiggMNURERBQRGGoGyGaz4YUXXuAHZ3bDmvhjTfyxJv5YE3+siT/WJLARtVCYiIiIIhdnaoiIiCgiMNQQERFRRGCoISIioojAUENEREQRgaGmF+fPn8fSpUuRnJwMu92Ou+++G8eOHTNer6+vx/z585GRkQGbzYasrCysX7++z58vJSJYsGABFEXBRx99ZNIoQsusmjzxxBO4+eabERsbi5SUFCxduhRnz541ezghYUZNGhoa8Mwzz2DKlCmIi4vDuHHj8Jvf/AbNzc2DMaQBM+v3ZNeuXZgzZw7sdjsURUFTU5PJIwkds2ricDjwzDPPIDk5GfHx8ViyZAkuX75s9nBCoreanDp1Cg8//DCysrIQGxuLqVOn4o033ui13QsXLuCBBx5ASkoK7HY7HnroIVRXV5s5lJAxqyZVVVVYvXo10tPTER8fjxkzZuCDDz4wcyiDjqGmFwsXLoTL5cLRo0dRUFCAn/3sZ1i0aBGqqqoAuO9SvHTpUhw4cADnz5/H3r17kZeXh3Xr1vWp/R07doTsIxsGi1k1yc7Oxp49e3DmzBkcPnwYIoLc3FxomjYYwxoQM2pSUVGBiooKbN++HcXFxdi7dy8++eQTrFmzZrCGNSBm/Z50dHRg/vz5eP755wdjGCFlVk02btyI/fv3Y9++fThx4gTa2tqwaNGiiPjbKSgoQEpKCt59912UlJRg8+bN2LRpE3bu3Bmwzfb2duTm5kJRFBw9ehRfffUVnE4nFi9eDF3XB2toQTOjJgCwevVqnDt3DgcOHEBxcTGWLVuG5cuXo7CwcDCGNTiEAqqtrRUA8uWXXxrbWlpaBIDk5eUFPO6NN96QzMzMXtsvKiqSzMxMqaysFACyf//+UHTbVGbXpLtTp04JACktLQ26v4NhMGvy73//W6Kjo6Wrqyvo/g6GwajJsWPHBIA0NjYOtLuDwqyaNDU1idVqlX379hnbrly5IqqqyieffBKazpsk2Jo89dRT8utf/zrg64cPHxZVVaW5udnY1tDQIADkyJEjoem8ScyqiYhIfHy8vPPOOz7bkpKS5O233x5Yp4cQztTcwJgxYzB16lS88847aG9vh8vlwptvvom0tDRkZ2f3eExFRQU+/PBD5OTk3LDtjo4OPPzww9i5cyfS09PN6L4pzKxJd+3t7dizZw8mTJgw5D9VfbBqAgDNzc2w2+2IihraH9s2mDUZLsyqSUFBAbq6upCbm2tsy8jIwLRp03Dy5MmQjyOUgqkJ4P47SEpKCvi6w+GAoig+N6eLiYmBqqo4ceJESMcQambVBAB++ctf4v3330dDQwN0Xce+ffvgcDgwZ86cEI8ijMKdqoa6y5cvS3Z2tiiKIhaLRTIyMqSwsNBvvxUrVkhsbKwAkMWLF0tnZ+cN2127dq2sWbPGeI5hMlMjYl5NRET++te/Snx8vACQW265ZcjP0niZWROvuro6GTdunGzevDmEPTeP2TUZbjM1IubU5F//+pdER0f7bb///vtl7dq1oey+KfpaE6+TJ0+K1WqVTz/9NOA+NTU1YrfbZcOGDdLe3i5tbW3y9NNPC4ARWxMR96zevHnzBIBERUWJ3W7v9ZjhZkSGmhdeeEEA3PCRn58vuq7LkiVLZMGCBXLixAkpKCiQJ598UsaOHSsVFRU+bVZWVsqZM2fko48+kltvvVWefPLJgO//8ccfy6RJk6S1tdXYFu5QE+6aeDU1Ncn58+fliy++kMWLF8uMGTP69Y8/lIZKTUREmpubZdasWTJ//nxxOp1mDLdPhlJNhkqoCXdNAoWa++67T5544omQj7cvzKiJiMjp06clJSVFXnnllV77cPjwYZk4caIRDB555BGZMWNGn3+/Qm0o1GT9+vVy1113SV5enhQVFcmLL74oiYmJ8v3335sx5LAYkaGmtrZWzpw5c8NHZ2en5OXl+Z2XFRGZNGmSbN26NWD7x48fFwA9/gKKiGzYsMH4Q/M+AIiqqpKTkxPKofZZuGvSE4fDIXFxcfLee+8FPa6BGCo1aWlpkdmzZ8vcuXPDFvC8hkpNRIZOqAl3TT777DMBIA0NDT7bp0+fLn/84x8HPsAgmFGTkpISSU1Nleeff77fffH+jqSlpcm2bdsGNLZghbsmpaWlAkBOnz7ts33u3LlhC79mGNon5k2SnJyM5OTkXvfr6OgA4L4ioTtVVW+4gl48H6flcDh6fP0Pf/gDHn/8cZ9tt99+O/7yl79g8eLFvfbLDOGuyY2O6+8xoTIUatLS0oJ58+bBZrPhwIEDiImJ6UvXTTMUajLUhLsm2dnZsFqtOHLkCB566CEAQGVlJU6fPo1t27b1aQyhFuqalJSU4N5778Vjjz2GV199td99AYCjR4+ipqYGS5Ys6dfxoRLumgRq12KxDIsrwvosnIlqqKutrZUxY8bIsmXLpKioSM6dOye//e1vxWq1SlFRkYiIHDx4UHbv3i3FxcVSVlYmBw8elNtuu03uvvtuo53Lly/LlClT5Jtvvgn4Xhgma2rMqsmFCxdky5Yt8u2338rFixfl5MmTsnTpUklKSpLq6uqwjLWvzKpJS0uLzJo1S26//XYpLS2VyspK4+FyucIy1r4y82+nsrJSCgsL5a233jKuEiksLJT6+vpBH2d/mFmTdevWSWZmpuTl5cl3330n9957r/z0pz+NiN8T7+mVVatW+fwN1NTUGO30VJPdu3fL119/LaWlpfLPf/5TkpKS5Nlnnx30MfaXWTVxOp0yadIkueeee+Sbb76R0tJS2b59uyiKIgcPHgzLWM3AUNOL/Px8yc3NlaSkJElISJCf//zncujQIeP1o0ePyuzZsyUxMVFiYmJk8uTJ8txzz/lMiZeVlQkAOXbsWMD3GS6hRsScmly5ckUWLFggqampYrVaJTMzU1auXClnz54d5NEFx4yaeE+v9PQoKysb3AEGway/nUBrE/bs2TN4gwuSWTXp7OyU9evXS1JSksTGxsqiRYukvLx8EEcWvN5qEujnPX78eGOfnmry3HPPSVpamlitVpk8ebL8+c9/Fl3XB3FkwTOrJufPn5dly5ZJamqqxMXFyfTp0/0u8R7uFBHP3CYRERHRMMb71BAREVFEYKghIiKiiMBQQ0RERBGBoYaIiIgiAkMNERERRQSGGiIiIooIDDVEREQUERhqiIiIKCIw1BAREVFEYKghIiKiiMBQQ0RERBGBoYaIiIgiwv8DbrvUcQyeGzsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "above is vtd-snapped plan weight, below is muni-snapped\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Adding Delaware voting here.  county =\",mC)\n",
    "regionLists, regionLists3, regionStarters = Delaware2LISTS.copy(), Delaware2LISTS3.copy(), Delaware2Starters.copy()\n",
    "#  ***ALTER ABOVE FOR EACH REGION ******\n",
    "regionVoters, regionVoters3 = countyTractList[mC].copy(), countyTractList[mC].copy()\n",
    "for ppC in pairedCountyList:\n",
    "    altCPlist = [countyPCP[ppC] for v in countyTractList[ppC] ]\n",
    "    ppCvoters  = getPickyPCvoters(countyTractList[ppC], altCPlist, tractCP, tractPop, regionLists)\n",
    "    ppCvoters3 = getPickyPCvoters(countyTractList[ppC], altCPlist, tractCP, tractPop, regionLists3)\n",
    "    regionVoters  += ppCvoters\n",
    "    regionVoters3 += ppCvoters3\n",
    "    for v in countyTractList[ppC]:\n",
    "        if v in ppCvoters:\n",
    "            plt.text(tractCP[v].x, tractCP[v].y,\"IN\")\n",
    "        else:\n",
    "            plt.text(tractCP[v].x, tractCP[v].y,\"X\")\n",
    "        if v in ppCvoters3:\n",
    "            plt.text(tractCP[v].x, tractCP[v].y+0.008,\"in3\",fontsize=6)\n",
    "    plotPoly(countyGeom[ppC])\n",
    "plt.show()\n",
    "\n",
    "regionWeights =  setPlanWeights(regionVoters, regionLists, tractCP, tractPop3)\n",
    "regionWeights3 = setPlanWeights(regionVoters3, regionLists3, tractCP, tractPop3)\n",
    "for i,v in enumerate(regionStarters):\n",
    "    if regionWeights[i] == 0:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,\"o\",fontsize=4)\n",
    "    else:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,r3(regionWeights[i]),fontsize=8)\n",
    "    if regionWeights[i] == np.max(regionWeights):\n",
    "        for j,L in enumerate(regionLists[i]):\n",
    "            dGeom = tractGeom[L[0]]\n",
    "            for t in L:\n",
    "                dGeom = dGeom.union(tractGeom[t])\n",
    "            plotPoly(dGeom)\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()\n",
    "print(\"above is vtd-snapped plan weight, below is muni-snapped\")\n",
    "for i,v in enumerate(regionStarters):\n",
    "    if regionWeights3[i] == 0:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,\"o\",fontsize=4)\n",
    "    else:\n",
    "        plt.text(tractCP[v].x, tractCP[v].y,r3(regionWeights3[i]),fontsize=8)\n",
    "    if regionWeights3[i] == np.max(regionWeights3):\n",
    "        for j,L in enumerate(regionLists3[i]):\n",
    "            dGeom = tractGeom[L[0]]\n",
    "            for t in L:\n",
    "                dGeom = dGeom.union(tractGeom[t])\n",
    "                if j == 0: #ID the tracts in the straddler\n",
    "                    plotPoly(tractCP[t].buffer(0.002))\n",
    "            plotPoly(dGeom)\n",
    "plotPoly(countyGeom[mC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "262780a3-747b-4fbd-91f6-c3131f7032fe",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the HD weights to a file for region Delaware\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the HD weights to a file for region\",regionName[currentRegionNo])\n",
    "date = \"19Mar\"\n",
    "rLISTS, rWeights = [regionLists.copy(), regionLists3.copy()], [ regionWeights.copy(), regionWeights3.copy() ]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+regionName[currentRegionNo]+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "1acee2ad-1cce-4171-9ab9-b5063dcf9273",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On to NE Ohio\n",
      "There are two compact schemes.  The simpler one (A) links Cuy-Gea, Gea-Asht, Asht-Trumb.  Summ-Strk-Port are combined\n",
      "In terms of county pairs, we have 17-27, 27-3, 3-77, and 76-66-75\n",
      "NOTE: THIS SCHEME A FAILS FOR SENATE PAIRING RULES.  Therefore, we will not use; archive at end of this file\n",
      "The second option is Cuy-Summ, Trum-Asht, Asht-Gea, Gea-Por-Stark, which necessitates a vertical slice of Por\n",
      "These are county pairs 17-76, 77-3, 3-27, 27-66-75 (with 27 cut along SW-NE diag or E-W)\n",
      "For this second option, only the eastern slice of Portage will work compactly; the western edge is too populous\n",
      "For each of these two options, we will pick two extreme 0.61aDP bites out of east or south Cuyahoga, then ...\n",
      "... compute the 10-district Home Districts within the remaining 10-district Cuyahoga, using std vtd or muni Home D's\n",
      "However, BOTH OF THESE CREATE A SENATE FAIL.  To satisfy Senate, Trum-Port pair req'd\n",
      "Therefore, se must overpopulate Cuya with Geauga+Summit or Lake.  Huffman's 9/9...\n",
      "..=our option C pairs Cuya-Gea-Summ, Gea-Asht.  Trum-Port.  Stark's 0.15 paired with rural county\n",
      "If we instead pair Cuya w Medina vs. Cuya-Summit, then Summit fails Senate\n",
      "This third option creates mild constraint (high pop) in Cuya, but not as severe as other Huffman/ORC options; e.g. enacted\n",
      "Scheme C's constraints are mildly relieved by giving Lake a ~0.08 piece of Ashtabula\n",
      "The schemeC pairings are thus 17-27-76, 27-3, 3-42.  66-77.  75-37 or 75-78. The Lake cut always has SW corner in full district.\n",
      "The 2022 enacted pairs Lake with Cuyahoga, which exacerbates the pop constraint, but keeps Solon whole\n",
      "voting is tbd, but could be as follows:\n",
      "each vtd votes for the district (from all districts it's drawn into in any regional plan) which puts that vtd closest to the PCP\n",
      "if the vtd-PCP distance of multiple districts is within 1% of minimum, that district is appended to the voted-for list\n",
      "if the voted-for list includes both A & B plans, we coin-flip to decide whether this vtd prefers an A or B plan\n",
      "once a vtd picks A or B, that vtd votes on its own district and adjacently-forced districts, as follows:\n",
      "it votes indirectly on coupled districts, distributing its vote among the possible coupled configurations with tbd weighting\n",
      "for example: a Cuyahoga that votes for an option A straddler votes for all 3-77 + 27-3 possibilities, with its voting weight\n",
      " distributed among the 27-3 and 3-77 choices proportionally to the relative 27, 3, and 77 votes for those 2-district plans.\n",
      "Alternatively, we could devise a weighted random voting.  If I'm in district 1, I vote for 2a-3a vs. 2b-3b based on 2 & 3 prefs\n"
     ]
    }
   ],
   "source": [
    "print(\"On to NE Ohio\")\n",
    "print(\"There are two compact schemes.  The simpler one (A) links Cuy-Gea, Gea-Asht, Asht-Trumb.  Summ-Strk-Port are combined\")\n",
    "print(\"In terms of county pairs, we have 17-27, 27-3, 3-77, and 76-66-75\")\n",
    "print(\"NOTE: THIS SCHEME A FAILS FOR SENATE PAIRING RULES.  Therefore, we will not use; archive at end of this file\")\n",
    "\n",
    "print(\"The second option is Cuy-Summ, Trum-Asht, Asht-Gea, Gea-Por-Stark, which necessitates a vertical slice of Por\") #OK for Senate\n",
    "#   This is the same scheme as in Sykes' 9/2 map\n",
    "print(\"These are county pairs 17-76, 77-3, 3-27, 27-66-75 (with 27 cut along SW-NE diag or E-W)\")\n",
    "print(\"For this second option, only the eastern slice of Portage will work compactly; the western edge is too populous\")\n",
    "print(\"For each of these two options, we will pick two extreme 0.61aDP bites out of east or south Cuyahoga, then ...\")\n",
    "print(\"... compute the 10-district Home Districts within the remaining 10-district Cuyahoga, using std vtd or muni Home D's\")\n",
    "print(\"However, BOTH OF THESE CREATE A SENATE FAIL.  To satisfy Senate, Trum-Port pair req'd\")\n",
    "print(\"Therefore, se must overpopulate Cuya with Geauga+Summit or Lake.  Huffman's 9/9...\")\n",
    "print(\"..=our option C pairs Cuya-Gea-Summ, Gea-Asht.  Trum-Port.  Stark's 0.15 paired with rural county\")\n",
    "print(\"If we instead pair Cuya w Medina vs. Cuya-Summit, then Summit fails Senate\")\n",
    "print(\"This third option creates mild constraint (high pop) in Cuya, but not as severe as other Huffman/ORC options; e.g. enacted\")\n",
    "print(\"Scheme C's constraints are mildly relieved by giving Lake a ~0.08 piece of Ashtabula\")\n",
    "print(\"The schemeC pairings are thus 17-27-76, 27-3, 3-42.  66-77.  75-37 or 75-78. The Lake cut always has SW corner in full district.\")\n",
    "print(\"The 2022 enacted pairs Lake with Cuyahoga, which exacerbates the pop constraint, but keeps Solon whole\")\n",
    "\n",
    "\n",
    "print(\"voting is tbd, but could be as follows:\")\n",
    "print(\"each vtd votes for the district (from all districts it's drawn into in any regional plan) which puts that vtd closest to the PCP\")\n",
    "print(\"if the vtd-PCP distance of multiple districts is within 1% of minimum, that district is appended to the voted-for list\")\n",
    "print(\"if the voted-for list includes both A & B plans, we coin-flip to decide whether this vtd prefers an A or B plan\")\n",
    "print(\"once a vtd picks A or B, that vtd votes on its own district and adjacently-forced districts, as follows:\")\n",
    "print(\"it votes indirectly on coupled districts, distributing its vote among the possible coupled configurations with tbd weighting\")\n",
    "print(\"for example: a Cuyahoga that votes for an option A straddler votes for all 3-77 + 27-3 possibilities, with its voting weight\")\n",
    "print(\" distributed among the 27-3 and 3-77 choices proportionally to the relative 27, 3, and 77 votes for those 2-district plans.\")\n",
    "print(\"Alternatively, we could devise a weighted random voting.  If I'm in district 1, I vote for 2a-3a vs. 2b-3b based on 2 & 3 prefs\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "05290d54-e099-4ad8-aabd-443b53da0f51",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Option C sequence: 1A-Lake cuts & pick Asht pieces for Lake pairing, compute Asht remnant for Geauga pairing\n",
      "1B-pick Geau options for Asht pairing  1C-pair Geau remnant with Cuya and Summit\n",
      "2: create Port-Trum straddler with associated Port-only, Trum-only districts\n",
      "3-tetrasect Summit.  4-clip off 0.15 Stark, then tri-sect Stark remnant.  Ignore Stark straddler; always will be red\n",
      "5-use DRA to draw some compact Cuyahoga options with Solon split\n",
      "Dividing CuyaLakeGeauAsht into 18 districts of pop 123934 which is 1.03984 of normal\n"
     ]
    }
   ],
   "source": [
    "print(\"Option C sequence: 1A-Lake cuts & pick Asht pieces for Lake pairing, compute Asht remnant for Geauga pairing\")\n",
    "print(\"1B-pick Geau options for Asht pairing  1C-pair Geau remnant with Cuya and Summit\")\n",
    "print(\"2: create Port-Trum straddler with associated Port-only, Trum-only districts\")\n",
    "print(\"3-tetrasect Summit.  4-clip off 0.15 Stark, then tri-sect Stark remnant.  Ignore Stark straddler; always will be red\")\n",
    "print(\"5-use DRA to draw some compact Cuyahoga options with Solon split\")\n",
    "CuyaLakeGeauAshtSummPop = countyPop[17]+countyPop[42]+countyPop[27]+countyPop[3]+countyPop[76]\n",
    "nD = int(CuyaLakeGeauAshtSummPop/aDP)\n",
    "RaDP = CuyaLakeGeauAshtSummPop / nD\n",
    "print(\"Dividing CuyaLakeGeauAsht into\",nD,\"districts of pop\",int(RaDP),\"which is\",r5(RaDP/aDP),\"of normal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a218163b-e759-46e4-a447-a74cf4b1317b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "option C.  Step 1A prep: visualizing western Ashtabula tracts\n",
      "8125 1376\n",
      "8147 2659\n",
      "8228 1735\n",
      "8230 1038\n",
      "8231 720\n",
      "8233 1624\n",
      "1813 1376.0 muni and pop\n",
      "1816 10084.0 muni and pop\n",
      "1833 2587.0 muni and pop\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"option C.  Step 1A prep: visualizing western Ashtabula tracts\")\n",
    "for v in countyTractList[3]:\n",
    "    if tractGeom[v].intersects(countyGeom[42]) or tractGeom[v].intersects(countyGeom[27]):\n",
    "        #plotPoly(tractGeom[v])\n",
    "        plt.text(tractGeom[v].centroid.x, tractGeom[v].centroid.y,v, fontsize=6, ha='center')\n",
    "        print(v,tractPop3[v])\n",
    "for m in countyMuniList[3]:\n",
    "    if muniGeom[m].intersects(countyGeom[42]):\n",
    "        plotPoly(muniGeom[m])\n",
    "        plotCenter(muniPop[m],muniGeom[m])\n",
    "        print(m,muniPop[m],\"muni and pop\")\n",
    "plotPoly(countyGeom[3])\n",
    "plotPoly(countyGeom[42])\n",
    "for v in countyTractList[42]:\n",
    "    if tractGeom[v].intersects(countyGeom[3]):\n",
    "        plotCenter(v,tractGeom[v])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "43ec73b0-34c7-4e88-9f20-c11eaaa6a1bc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "starting option C. Create multiple Lake-only options, and two Lake-Asht options\n",
      "Note, I tried different Asht skew ratios but Geneva always picked\n",
      "for Lake-Ash 2-district, targ pop is 121068.95373737374 +/- 4076\n",
      "angle 54.0 led to a good Lake district with muni pop 120310.0 splits= 1\n",
      "POINT (-80.97420511129756 41.82466870486983) 8775 4076.7068686868733 cTcP, tgtPPCpop3, tolrPop\n",
      "here is a GOOD muni LakeAsht straddler for nSplits, ppC skew ratio of 1 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angle 99.0 led to a good Lake district with muni pop 119711.0 splits= 1\n",
      "POINT (-80.97420511129756 41.82466870486983) 8176 4076.7068686868733 cTcP, tgtPPCpop3, tolrPop\n",
      "here is a GOOD muni LakeAsht straddler for nSplits, ppC skew ratio of 1 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "angle 144.0 led to a good Lake district with muni pop 121023.0 splits= 1\n",
      "POINT (-80.97420511129756 41.82466870486983) 9488 4076.7068686868733 cTcP, tgtPPCpop3, tolrPop\n",
      "here is a GOOD muni LakeAsht straddler for nSplits, ppC skew ratio of 1 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All done creating Lake-Asht straddlers.  We had 3 successes out of 3\n"
     ]
    }
   ],
   "source": [
    "print(\"starting option C. Create multiple Lake-only options, and two Lake-Asht options\")\n",
    "print(\"Note, I tried different Asht skew ratios but Geneva always picked\")\n",
    "mC, ppC = 42, 3  #Lake, Ashtabula\n",
    "AshLakeLISTS, AshLakeLISTS3, AshLakeANGLES = list(), list(), list()\n",
    "wholeDistrictShadeRatio = 1.03  #how much we're willing to nudge up Lake whole districts to ease regional constraint\n",
    "nAngles, minAngle, maxAngle = 3, -0.7*math.pi, -0.2*math.pi\n",
    "LakeAngles = [minAngle+ (maxAngle-minAngle)*float(angleNo)/(nAngles-1) for angleNo in range(nAngles)]\n",
    "rejectTract = 4771  #whole Lake district should exclude this tract; if not, use complementary angle\n",
    "tgtPPCpop = 0.08*aDP  #forcing a certain fraction of Ashtabula to ease Cuyahoga constraint\n",
    "targPop = 0.5* (countyPop[mC] + tgtPPCpop )\n",
    "tolrPop = min(targPop - 0.95*aDP, 1.05*aDP - targPop)\n",
    "print(\"for Lake-Ash 2-district, targ pop is\",targPop,\"+/-\",int(tolrPop))\n",
    "for i, sliceAngle in enumerate(LakeAngles):    \n",
    "    if angledPentaPoly(countyPCP[mC],sliceAngle,5.*countyGeom[mC].area).contains(tractCP[rejectTract]):\n",
    "        sliceAngle += math.pi  #whole Lake district should exclude this tract; if not, use complementary angle\n",
    "    cutPoint = countyPCP[mC]\n",
    "    halfList, halfPop = cutVtdSnap(targPop, tolrPop, tractCP, countyTractList[mC], neighborList, tractPop, sliceAngle, cutPoint)\n",
    "\n",
    "    halfList3, hML, halfPop3, splitMuni = cutMuniSnap(targPop, tolrPop, muniTractList,tractCP, countyTractList[mC],\n",
    "                                                  muniNeighborList,tractPop3, muniPop, muniPCP, sliceAngle, cutPoint)\n",
    "    popGap = targPop - halfPop3\n",
    "    list3B, pop3B,nLakeSplits = list(),0.,0\n",
    "    if abs(popGap) > tolrPop :  #SPECIAL for Lake, we allow a single split; Mentor is problematic to skirt.\n",
    "        splitCutPt = muniPCP[splitMuni]\n",
    "        list3B, pop3B = cutVtdSnap(popGap, tolrPop,tractCP, muniTractList[splitMuni], neighborList, tractPop, sliceAngle, splitCutPt)\n",
    "        halfList3, halfPop3 = halfList3 + list3B, halfPop3 + pop3B\n",
    "        nLakeSplits = 1\n",
    "    isJiggy = True\n",
    "    for i, pair in enumerate([ [halfList, halfPop], [halfList3, halfPop3] ]):\n",
    "        if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < 0.05*aDP):\n",
    "            isJiggy = False\n",
    "            print(\"contiguity or pop FAIL for 0-1 vtd-muni\",i,isContiguous(pair[0],neighborList),pair[1])\n",
    "    if isJiggy:\n",
    "        print(\"angle\",r3(180./math.pi*sliceAngle),\"led to a good Lake district with muni pop\",halfPop3,\"splits=\",nLakeSplits)\n",
    "        mClist, mClist3,mCpop, mCpop3 = list(), list(), 0., 0.\n",
    "        for v in countyTractList[mC]:\n",
    "            if v not in halfList:\n",
    "                mClist.append(v)\n",
    "                mCpop += tractPop3[v]\n",
    "            if v not in halfList3:\n",
    "                mClist3.append(v)\n",
    "                mCpop3 += tractPop3[v]\n",
    "        tgtPPCpop0, tgtPPCpop3 = targPop - mCpop, targPop - mCpop3\n",
    "        swellStarters = [8228]\n",
    "        skewRatioList = [1]  #[0.5, 1., 2.]  #these don't move the needle for Asht\n",
    "        bannedList = [8147]  #need a connector to Geauga, so leave SW corner open\n",
    "        ppCTL = countyTractList[ppC].copy()\n",
    "        ppCML = countyMuniList[ppC].copy()\n",
    "        for v in countyTractList[ppC]:\n",
    "            if v in bannedList:\n",
    "                ppCTL.remove(v)\n",
    "                if tractMuniNo[v] in ppCML:\n",
    "                    ppCML.remove(tractMuniNo[v])\n",
    "\n",
    "        for ppCxyR in skewRatioList: \n",
    "            cTcP = tractCP[swellStarters[0]] #= tractCP[ getClosestTract(tractCP[t], countyTractList[ppC],tractCP) ]\n",
    "            print(cTcP, int(tgtPPCpop3),tolrPop,\"cTcP, tgtPPCpop3, tolrPop\")\n",
    "            ppClist, ppCpop = vtdSwell(cTcP, tgtPPCpop0, tolrPop, tractCP, ppCTL,neighborList,tractPop3,ppCxyR) \n",
    "            ppClist3, ppCpop3, totSplits = muniSwell(cTcP, tgtPPCpop3, tolrPop, aDP, muniPop, muniPCP, muniTractList,\n",
    "                                                    muniNeighborList,ppCML, tractCP, neighborList, tractPop3, nLakeSplits, ppCxyR)\n",
    "\n",
    "            stradList, stradList3 = mClist + ppClist, mClist3 + ppClist3\n",
    "            stradPop, stradPop3 =   mCpop  + ppCpop,  mCpop3  + ppCpop3\n",
    "            if isContiguous(stradList, neighborList) and isContiguous(stradList3, neighborList):\n",
    "                if abs(stradPop - aDP) < 0.05 * aDP and abs(stradPop3 - aDP) < 0.05 * aDP and totSplits <= 1:\n",
    "                    AshLakeLISTS.append([halfList,stradList])\n",
    "                    AshLakeLISTS3.append([halfList3,stradList3])\n",
    "                    AshLakeANGLES.append(sliceAngle)\n",
    "                    for v in stradList3:\n",
    "                        plotPoly(tractGeom[v])\n",
    "                    plotPoly(countyGeom[mC])\n",
    "                    plotPoly(countyGeom[ppC])\n",
    "                    plotPoly(tractCP[t].buffer(0.005))\n",
    "                    print(\"here is a GOOD muni LakeAsht straddler for nSplits, ppC skew ratio of\",totSplits,ppCxyR)\n",
    "                    plt.show()\n",
    "                else:\n",
    "                    print(\"FAIL for pop or nSplits, which were\",stradPop, stradPop3, totSplits,\"for angle, skew=\",sliceAngle,ppCxyR)\n",
    "            else:\n",
    "                print(\"FAIL for vtd or muni contiguity for angle, skew=\",sliceAngle,ppCxyR)\n",
    "print(\"All done creating Lake-Asht straddlers.  We had\",len(AshLakeLISTS),\n",
    "      \"successes out of\",nAngles*len(swellStarters)*len(skewRatioList) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "fc14902e-5527-4f45-9b86-e06396e69e27",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the AshLake lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the AshLake lists to a file.  We will vote later\" )  \n",
    "currentRegionNo = 9  #Lake was separate\n",
    "date = \"30Apr\"\n",
    "rLISTS = [AshLakeLISTS.copy(), AshLakeLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo = list(), list()\n",
    "    #for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        #dRegionNo.append(currentRegionNo)\n",
    "        #dTL.append(DISTRICT_LIST)\n",
    "\n",
    "    for p,plan in enumerate(PLAN_LISTS):  #Use this block for multi-district plans\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"AshLake\"+\"_\"+rType[i]+\"C.csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "f19bcd99-f459-472b-b220-fa8289ae8d26",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time for Lake tracts to vote.  Asht straddlers don't influence, so don't vote\n",
      "Voting done.  The most popular plan was 0 with 0.457 of the vote\n",
      "Voting done.  The most popular plan was 2 with 0.464 of the vote\n",
      "let us write the HD weights to a file for sub-region Lake Lake\n"
     ]
    }
   ],
   "source": [
    "print(\"time for Lake tracts to vote.  Asht straddlers don't influence, so don't vote\" )  \n",
    "currentRegionNo, fName = 9, \"Lake\"  #Lake was separate\n",
    "areaVoters = countyTractList[42] \n",
    "rLISTS = [AshLakeLISTS.copy(), AshLakeLISTS3.copy() ]\n",
    "rWeights =  [setPlanWeights(areaVoters,  rLISTS[0], tractCP, tractPop3), setPlanWeights(areaVoters, rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"let us write the HD weights to a file for sub-region\",fName,regionName[currentRegionNo])\n",
    "date = \"30April\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b6c2b68e-cec8-4468-8be8-581df865da2f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, create the Asht-Geau straddler.  Cutsnap, preserving west corner\n",
      "tgt, tgt3, tolr, tolr3 for Geau piece = 31804.38888888889 36444.38888888889 1211.272 1211.272\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 0 -90.0\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 0 -67.5\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 0 -45.0\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 0 -22.5\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 0 0.0\n",
      "tgt, tgt3, tolr, tolr3 for Geau piece = 32720.38888888889 36444.38888888889 1211.272 1211.272\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 1 -90.0\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 1 -67.5\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 1 -45.0\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 1 -22.5\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 1 0.0\n",
      "tgt, tgt3, tolr, tolr3 for Geau piece = 33598.38888888889 36444.38888888889 1211.272 1211.272\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 2 -90.0\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 2 -67.5\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 2 -45.0\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 2 -22.5\n",
      "GOOD AshtGeau for LakeStartNo,GeauAngle 2 0.0\n",
      "All done creating Asht-Geauga straddlers.  We had 15 successes out of 15\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, create the Asht-Geau straddler.  Cutsnap, preserving west corner\")\n",
    "AshtGeauCLISTS, AshtGeauCLISTS3, AGstarterLists = list(), list(), list()\n",
    "minAngle, maxAngle, nAngles = -0.5*math.pi, 0.*math.pi, 5  #NS, NW-SE, or W-E cut OK, not NE-SW\n",
    "GeauAngles = [minAngle + j/(nAngles-1.)*(maxAngle-minAngle) for j in range(nAngles)]\n",
    "mC, ppC = 3, 27\n",
    "bannedT = 8147  #SW Geauga corner cannot be included in AshtGeau straddler; leave for Cuya-Summ-Geau\n",
    "for iii, trigger in enumerate(AshLakeLISTS):\n",
    "    mClist, mClist3 = countyTractList[mC].copy(), countyTractList[mC].copy()\n",
    "    mCpop, mCpop3 = countyPop[mC], countyPop[mC]\n",
    "    for L in AshLakeLISTS[iii]:\n",
    "        for v in L:\n",
    "            if v in countyTractList[mC]:\n",
    "                mClist.remove(v)\n",
    "                mCpop -= tractPop3[v]\n",
    "    for L in AshLakeLISTS3[iii]:\n",
    "        for v in L:\n",
    "            if v in countyTractList[mC]:\n",
    "                mClist3.remove(v)\n",
    "                mCpop3 -= tractPop3[v]\n",
    "    tgtPPCpop, tgtPPCpop3 = RaDP-mCpop, RaDP-mCpop3  #load 'em up!\n",
    "    tolr, tolr3 = 1.05*aDP - RaDP, 1.05*aDP - RaDP\n",
    "    print(\"tgt, tgt3, tolr, tolr3 for Geau piece =\",tgtPPCpop, tgtPPCpop3,r3(tolr),r3(tolr3) )\n",
    "    bannedCP = tractCP[bannedT]\n",
    "    for angle in GeauAngles:\n",
    "        if angledPentaPoly(countyPCP[ppC],angle, 5*countyGeom[ppC].area**0.5 ).disjoint(bannedCP):\n",
    "            angle += math.pi\n",
    "        cutPoint = countyPCP[ppC]\n",
    "        ppClist, ppCpop = cutVtdSnap(tgtPPCpop, tolr, tractCP, countyTractList[ppC], neighborList,tractPop3, angle, cutPoint)\n",
    "        ppClist3, pML, ppCpop3, sM = cutMuniSnap(tgtPPCpop, tolr, muniTractList,tractCP, countyTractList[ppC],\n",
    "                                                 muniNeighborList,tractPop3, muniPop, muniPCP, angle, cutPoint)\n",
    "        popGap, fillPop = tgtPPCpop - ppCpop3, 0.\n",
    "        if abs(popGap) > tolr3 :  #try alternate muni's, do not allow a split at this stage\n",
    "            hT = getFarthestTract(cutPoint, ppClist3, tractCP)   #\"home\" tract to bias fill-in toward compactness\n",
    "            vlist, fillPop = getFillList3noSplit(hT, popGap, tolr3,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                 pML,countyMuniList[ppC], tractCP, neighborList, tractPop3)\n",
    "            ppClist3 += vlist\n",
    "            ppCpop3  += fillPop\n",
    "            \n",
    "        stradList, stradList3 = mClist + ppClist, mClist3 + ppClist3\n",
    "        stradPop, stradPop3   = mCpop + ppCpop ,    mCpop3 + ppCpop3\n",
    "        if isContiguous(stradList, neighborList) and isContiguous(stradList3, neighborList):\n",
    "            if abs(stradPop - aDP) < 0.05 * aDP and abs(stradPop3 - aDP) < 0.05 * aDP :  #and totSplits <= 1:\n",
    "                AshtGeauCLISTS.append(stradList)\n",
    "                AshtGeauCLISTS3.append(stradList3)\n",
    "                AGstarterLists.append(iii)\n",
    "                print(\"GOOD AshtGeau for LakeStartNo,GeauAngle\",iii,r3(180/3.1416*angle))\n",
    "            else:\n",
    "                print(\"pop FAIL for AshtGeau for LakeStartNo,GeauAngle\",iii,r3(180/3.1416*angle))\n",
    "                print(mCpop3, ppCpop3, stradPop3, stradPop,\"mcPop3, ppCpop3, stradPop3, stradPop\")\n",
    "        else:\n",
    "            print(\"contig FAIL AshtGeau for LakeStartNo,GeauAngle\",iii,r3(180/3.1416*angle))\n",
    "            for v in stradList3:\n",
    "                plotPoly(tractGeom[v])\n",
    "            plotPoly(countyGeom[mC])\n",
    "            plotPoly(countyGeom[ppC])\n",
    "            plotPoly(countyGeom[tpC])\n",
    "            plt.show()\n",
    "print(\"All done creating Asht-Geauga straddlers.  We had\",len(AshtGeauCLISTS),\n",
    "      \"successes out of\",len(AshLakeLISTS)*nAngles )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "b89c3212-09b1-4049-8edb-9dec16083f6d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the AshtGeau lists to a file, tagging the AG starter  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the AshtGeau lists to a file, tagging the GL starter  We will vote later\" )  \n",
    "currentRegionNo = 1  #Lake was separate\n",
    "date = \"30Apr\"\n",
    "rLISTS = [AshtGeauCLISTS.copy(), AshtGeauCLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, starterL = list(), list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "        starterL.append(AGstarterLists[j])\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL,\"AGstarter\":starterL} ) \n",
    "    outname = STATE+date+\"AshtGeau\"+\"_\"+rType[i]+\"C.csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "30ae2275-16c7-4d43-893f-588e62ab14cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing one of the 15 optionC Asht-Gea straddlers\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing one of the 15 optionC Asht-Gea straddlers\")\n",
    "n = random.randint(15)\n",
    "for v in AshtGeauCLISTS3[n]:\n",
    "    plotPoly(tractGeom[v])\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyGeom[ppC])\n",
    "plotCenter(n,countyGeom[ppC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "45b23973-5f26-4665-8671-9839784530f5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(countyGeom[76])\n",
    "for v in countyTractList[76]:\n",
    "    if tractGeom[v].intersects(countyGeom[27]):\n",
    "        plotPoly(tractGeom[v])\n",
    "        plotCenter(v,tractGeom[v])\n",
    "plotPoly(countyGeom[76])\n",
    "plotPoly(countyGeom[17])\n",
    "for v in countyTractList[17]:\n",
    "    if tractGeom[v].intersects(tractGeom[6459]):\n",
    "        plotPoly(tractGeom[v])\n",
    "        plotCenter(v,tractGeom[v])\n",
    "plotPoly(countyGeom[17])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "92fccbc1-c01f-46e7-8316-3196b29e5119",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, the GeauCuyaSummit straddler.  Cuya is least constrained, Geau is forced remnant, so start in Summit\n",
      "Swell Summit out of the corner that touches Geauga\n",
      "pop FAIL for SummCuyaGea for AshtGeau no, Summ and Cuya skew ratios 11 0.5 2.0\n",
      "mC,ppC,tpC, totPop's are 44350 19004 61961 125315\n",
      "mC,ppC,tpC, totPop3's are 44241.0 18107 61128 123476.0\n",
      "pop FAIL for SummCuyaGea for AshtGeau no, Summ and Cuya skew ratios 11 1.0 2.0\n",
      "mC,ppC,tpC, totPop's are 44415 19004 61961 125380\n",
      "mC,ppC,tpC, totPop3's are 44241.0 18107 61128 123476.0\n",
      "pop FAIL for SummCuyaGea for AshtGeau no, Summ and Cuya skew ratios 14 0.5 2.0\n",
      "mC,ppC,tpC, totPop's are 44350 19004 61942 125296\n",
      "mC,ppC,tpC, totPop3's are 44241.0 18107 61284 123632.0\n",
      "pop FAIL for SummCuyaGea for AshtGeau no, Summ and Cuya skew ratios 14 1.0 2.0\n",
      "mC,ppC,tpC, totPop's are 44415 19004 61942 125361\n",
      "mC,ppC,tpC, totPop3's are 44241.0 18107 61284 123632.0\n",
      "All done creating SummCuyaGea straddlers.  We had 131 successes out of 135\n",
      "We also captured 3 unique Summit straddler pieces\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, the GeauCuyaSummit straddler.  Cuya is least constrained, Geau is forced remnant, so start in Summit\")\n",
    "print(\"Swell Summit out of the corner that touches Geauga\")\n",
    "mC, ppC, tpC = 76, 17, 27  #Summit, Cuya, Geau\n",
    "tgtMCpop = countyPop[mC]%RaDP\n",
    "# For each Cuya, tailor the tgtPop to total RaDP based on Summ and Geau pieces.  Allow Solon to be split\n",
    "#tgtPPCpop = countyPop[ppC]%RaDP\n",
    "#tgtTPCpop = RaDP - tgtPPCpop - tgtMCpop\n",
    "#tgtPop =  tgtMCpop + tgtPPCpop + tgtTPCpop\n",
    "triToler = min(1.05*aDP - RaDP, RaDP - 0.95*aDP)\n",
    "SCGtriLists, SCGtriLists3, SCGstarterLists = list(), list(), list()  #SCGstarter is the index of the AshtGeauCLISTS)\n",
    "SummStradLists, SummStradLists3 = list(), list()  #capture each distinct Summit straddler option for later Summit divvying\n",
    "mChT, ppChT = 6459, 5036  #NE Summit, SE Cuyahoga must be in straddler; these are \"home tracts\" for their straddler pieces\n",
    "skewRatios = [0.5, 1.0, 2.0 ]\n",
    "for ii in range(len(AshtGeauCLISTS)):  #include non-AshtGeau Geau tracts in the SummitCuyaGeauga straddler\n",
    "    tpCpop, tpCpop3, tpClist, tpClist3 = 0,0, list(), list()\n",
    "    for v in countyTractList[tpC]:\n",
    "        if v not in AshtGeauCLISTS[ii]:\n",
    "            tpCpop += tractPop3[v]\n",
    "            tpClist.append(v)\n",
    "        if v not in AshtGeauCLISTS3[ii]:\n",
    "            tpCpop3 += tractPop3[v]\n",
    "            tpClist3.append(v)\n",
    "\n",
    "    for iii, mCxyR in enumerate(skewRatios):\n",
    "        homeM = tractMuniNo[mChT]\n",
    "        cTcP = tractCP[mChT]\n",
    "        mClist, mCpop = vtdSwell(cTcP, tgtMCpop, triToler, tractCP, countyTractList[mC],neighborList,tractPop3,mCxyR)   \n",
    "        mClist3, mCpop3, totSplits = muniSwell(cTcP, tgtMCpop, triToler, aDP, muniPop, muniPCP, muniTractList,\n",
    "                                               muniNeighborList,countyMuniList[mC], tractCP, neighborList, tractPop3, 1, mCxyR)\n",
    "        #don't allow split here; save for Cuyahoga  \n",
    "        tgtPPCpop, tgtPPCpop = RaDP - mCpop - tpCpop, RaDP - mCpop3 - tpCpop3\n",
    "        for ppCxyR in skewRatios:\n",
    "            cTcP = tractCP[ppChT]\n",
    "            ppClist, ppCpop = vtdSwell(cTcP, tgtPPCpop, triToler, tractCP, countyTractList[ppC],neighborList,tractPop3,ppCxyR)   \n",
    "            ppClist3, ppCpop3, totSplits = muniSwell(cTcP, tgtPPCpop, triToler, aDP, muniPop, muniPCP, muniTractList,\n",
    "                                                   muniNeighborList,countyMuniList[ppC], tractCP, neighborList, tractPop3, 0, ppCxyR)\n",
    "            #allowing a Cuyahoga split\n",
    "\n",
    "            stradList, stradList3 = mClist + ppClist + tpClist, mClist3 + ppClist3 + tpClist3\n",
    "            stradPop, stradPop3   = mCpop + ppCpop + tpCpop,    mCpop3 + ppCpop3 + tpCpop3\n",
    "            if isContiguous(stradList, neighborList) and isContiguous(stradList3, neighborList):\n",
    "                if abs(stradPop - aDP) < 0.05 * aDP and abs(stradPop3 - aDP) < 0.05 * aDP :  #and totSplits <= 1:\n",
    "                    SCGtriLists.append(stradList)\n",
    "                    SCGtriLists3.append(stradList3)\n",
    "                    SCGstarterLists.append(ii)\n",
    "                    if mClist not in SummStradLists:\n",
    "                        SummStradLists.append(mClist)\n",
    "                        SummStradLists3.append(mClist3)\n",
    "                    #print(\"GOOD SummCuyaGea for AshtGeau no, Summ and Cuya skew ratios\",ii,mCxyR, ppCxyR)\n",
    "                else:\n",
    "                    print(\"pop FAIL for SummCuyaGea for AshtGeau no, Summ and Cuya skew ratios\",ii,mCxyR, ppCxyR)\n",
    "                    print(\"mC,ppC,tpC, totPop's are\",mCpop, ppCpop, tpCpop, stradPop)\n",
    "                    print(\"mC,ppC,tpC, totPop3's are\",mCpop3, ppCpop3, tpCpop3, stradPop3)\n",
    "            else:\n",
    "                print(\"contig FAIL for SummCuyaGea for AshtGeau no, Summ and Cuya skew ratios\",ii,mCxyR, ppCxyR)\n",
    "                for v in stradList3:\n",
    "                    plotPoly(tractGeom[v])\n",
    "                plotPoly(countyGeom[mC])\n",
    "                plotPoly(countyGeom[ppC])\n",
    "                plotPoly(countyGeom[tpC])\n",
    "                plt.show()\n",
    "print(\"All done creating SummCuyaGea straddlers.  We had\",len(SCGtriLists),\n",
    "      \"successes out of\",len(skewRatios)*len(skewRatios)*len(AshtGeauCLISTS) )\n",
    "print(\"We also captured\",len(SummStradLists),\"unique Summit straddler pieces\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "8848f4e9-1d37-4afb-a2a8-72e601788a99",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Side block to prep for DRA -- how much Cuya is usually in the SCG muni straddler?\n",
      "I will compute the Cuya pop for the three most popular SCG muni straddlers (70+pct of total HDwt)\n",
      "total Cuya pop for this list is 15180\n",
      "total Cuya pop for this list is 15180\n",
      "total Cuya pop for this list is 18924\n"
     ]
    }
   ],
   "source": [
    "print(\"Side block to prep for DRA -- how much Cuya is usually in the SCG muni straddler?\")\n",
    "print(\"I will compute the Cuya pop for the three most popular SCG muni straddlers (70+pct of total HDwt)\")\n",
    "list1 = [6459, 6484, 6485, 6139, 6140, 6141, 6172, 6173, 6194, 6195, 6503, 6504, 6505,6506, 6507, 6543, 6544, 6545, 6546, 6547,\n",
    "         6548, 6133, 6180, 6181, 6302, 6303,6304, 6305, 6438, 6439, 6440, 6442, 6466, 6182, 6451, 6476, 6477, 5036, 4333, 4336,\n",
    "         4392, 4390, 4387, 4384, 4332, 4348, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453,\n",
    "         2459, 2460, 2461, 2462, 2463, 2464, 2465, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2477, 2479, 2481,\n",
    "         2486, 2490,2491, 2492, 2493, 2494, 2496, 2497, 2498, 2500, 2501, 2502, 2503, 2505, 2507, 2508,2509, 2511, 2512, 2513,\n",
    "         2514, 2516]\n",
    "\n",
    "list2 = [6459, 6484, 6485, 6139, 6140, 6141, 6172, 6173, 6194, 6195, 6503, 6504, 6505, 6506, 6507, 6543, 6544, 6545, 6546, 6547,\n",
    "         6548, 6133, 6180, 6181, 6302, 6303, 6304, 6305, 6438, 6439, 6440, 6442, 6466, 6182, 6451, 6476, 6477, 5036, 4336, 4333,\n",
    "         4390, 4392, 4332, 4384, 4387, 4348, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451, 2452, 2453,\n",
    "         2454, 2455, 2456, 2457, 2458, 2469, 2473, 2474, 2475, 2476, 2483, 2486, 2487, 2490, 2492, 2493, 2495, 2497, 2498, 2499,\n",
    "         2500, 2501, 2502, 2503, 2505, 2506, 2507, 2508, 2509, 2510, 2511, 2512, 2513, 2514, 2516, 2517]\n",
    "\n",
    "list3 = [6459, 6484, 6485, 6139, 6140, 6141, 6172, 6173, 6194, 6195, 6503, 6504, 6505, 6506, 6507, 6543, 6544, 6545, 6546, 6547,\n",
    "         6548, 6133, 6180, 6181, 6302, 6303, 6304, 6305, 6438, 6439, 6440, 6442, 6466, 6182, 6451, 6476, 6477, 5036, 4336, 4333,\n",
    "         4390, 4392, 4332, 4384, 4387, 4348, 4335, 4334, 2440, 2441, 2442, 2443, 2444, 2445, 2446, 2447, 2448, 2449, 2450, 2451,\n",
    "         2452, 2453, 2454, 2457, 2458, 2460, 2461, 2464, 2466, 2467, 2468, 2469, 2470, 2471, 2472, 2473, 2474, 2475, 2486, 2490,\n",
    "         2491, 2492, 2493, 2494, 2496, 2497, 2498, 2500, 2501, 2502, 2503, 2505, 2507, 2508, 2509, 2511, 2512, 2513, 2514, 2516]\n",
    "\n",
    "for L in [list1, list2, list3]:\n",
    "    CuyaPop = 0\n",
    "    for v in L:\n",
    "        if v in countyTractList[17]:\n",
    "            CuyaPop += tractPop3[v]\n",
    "    print(\"total Cuya pop for this list is\",CuyaPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8205ece8-0ca8-4f40-8c9d-e345bb082633",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "before we vote, divide Geaus into SCG choosers vs. Asht-Geau choosers\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"before we vote, divide Geaus into SCG choosers vs. Asht-Geau choosers\")\n",
    "choosers, Alist, Blist = countyTractList[27], AshtGeauCLISTS, SCGtriLists\n",
    "AGchoosers, GeauSCGchoosers = classifyStraddlers(countyTractList[27], Alist, Blist, tractCP, tractPop3)\n",
    "choosers, Alist, Blist = countyTractList[27], AshtGeauCLISTS3, SCGtriLists3\n",
    "AGchoosers3, GeauSCGchoosers3 = classifyStraddlers(countyTractList[27], Alist, Blist, tractCP, tractPop3)\n",
    "plotPoly(countyGeom[27])\n",
    "for v in AGchoosers:\n",
    "    plotPoly(tractCP[v].buffer(0.002))\n",
    "for v in GeauSCGchoosers:\n",
    "    plotCenter(\"SCG\",tractCP[v])\n",
    "plt.show()\n",
    "plotPoly(countyGeom[27])\n",
    "for v in AGchoosers3:\n",
    "    plotPoly(tractCP[v].buffer(0.002))\n",
    "for v in GeauSCGchoosers3:\n",
    "    plotCenter(\"SCG3\",tractCP[v])\n",
    "plt.show()      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "87adf4ee-eff0-481c-a58a-f9214b5f24cd",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, we will exclude Asht-Lake Ash'ers from AG voters, add in AG choosers, and AG-vote\n"
     ]
    }
   ],
   "source": [
    "#5/5/23 --NEXT STEP IS TO HAVE ASHT + GEAU AGCHOOSERS VOTE ON AG LISTS VIA SETPLAN1DWEIGHTS\n",
    "print(\"now, we will exclude Asht-Lake Ash'ers from AG voters, add in AG choosers, and AG-vote\")\n",
    "for v in countyTractList[3]:\n",
    "    if v not in AshLakeLISTS[1][1]:\n",
    "        AGchoosers.append(v)\n",
    "    if v not in AshLakeLISTS3[1][1]:\n",
    "        AGchoosers3.append(v)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "41af4691-1133-4270-98b1-ee22ec1b2a76",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Voting done.  The most popular district was 3 with 0.307 of the vote\n",
      "Voting done.  The most popular district was 3 with 0.287 of the vote\n",
      "Saving Asht-Geau vtd and muni lists with voting to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"time for AshtGeau choosers to vote. \" )  \n",
    "planLists = [AshtGeauCLISTS, AshtGeauCLISTS3]\n",
    "mC = 3  #Asht\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "regionalVotingList = [AGchoosers.copy(), AGchoosers3.copy()]\n",
    "reglVTDwts = set1DplanWeights(regionalVotingList[0], planLists[0], tractCP, tractPop3)\n",
    "reglMuniWts = set1DplanWeights(regionalVotingList[1], planLists[1], tractCP, tractPop3)\n",
    "print(\"Saving Asht-Geau vtd and muni lists with voting to a file\")\n",
    "#currentRegionNo = 7 #NE\n",
    "date = \"05May\"\n",
    "rLISTS = planLists.copy() \n",
    "rWeights = [reglVTDwts.copy(), reglMuniWts.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, voteFrac = list(), list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "        voteFrac.append(rWeights[i][j])\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":voteFrac,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"AshtGeauC\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "98e34268-412a-499d-b53e-7b7117ba4123",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the SCG lists to a file, tagging the GL starter  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the SCG lists to a file, tagging the GL starter  We will vote later\" )  \n",
    "currentRegionNo = 1  #Lake was separate\n",
    "date = \"30Apr\"\n",
    "rLISTS = [SCGtriLists.copy(), SCGtriLists3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, starterL = list(), list(), list()\n",
    "    #for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for multidistrict lists\n",
    "    #    dRegionNo.append(currentRegionNo)\n",
    "    #    dTL.append(DISTRICT_LIST)\n",
    "    #    starterL.append(SCGstarterLists[j])\n",
    "    dTL = PLAN_LISTS.copy()   #use this block instead for single-district lists not stored in brackets\n",
    "    starterL = SCGstarterLists.copy()\n",
    "    dRegionNo = [currentRegionNo for RRR in range(len(dTL)) ]\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL,\"SCGstarter\":starterL} ) \n",
    "    outname = STATE+date+\"SummCuyaGeau\"+\"_\"+rType[i]+\"C.csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "58b52cc0-c410-4716-829c-de0587bc20d9",
   "metadata": {},
   "source": [
    "4/30/23: thinking about voting for Asht-G.  First, have each Geauga vote among AG + GCS.\n",
    "For G's that vote for an AG, add them to non AL Asht's for the voting pool for the AG district.\n",
    "Then for Summit, divide into GCS-preferring vs. in-Summit preferring.\n",
    "All Summit-preferring are included on a vote for the best 4-district Summit plan.\n",
    "For Cuyahoga, simply assign the 6 always-in-GCS to the GCS voting pool, exclude the sometimes-in, sometimes-out Cuya's.\n",
    "These 6 Cuya's + GCS-preferring Summit + GCS-preferring Geau's vote on best GCS.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b3f7ceef-004a-4650-8585-5fe224685033",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing one of the 131 good SummCuyaGeau straddlers\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing one of the\",len(SCGtriLists),\"good SummCuyaGeau straddlers\")\n",
    "n = random.randint(len(SCGtriLists3))\n",
    "for v in SCGtriLists[n]:\n",
    "    plotPoly(tractGeom[v])\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyGeom[ppC])\n",
    "plotPoly(countyGeom[tpC])\n",
    "plotCenter(n,countyGeom[tpC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "974ece55-089c-4143-b7c0-f824991b8177",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FYI for later Cuya work; visualizing Cuya portion of one of the 131 good SummCuyaGeau straddlers\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "regardless of skew ratio, [5036,4333,4387,4348, 4392, 4384, 4332, 4390, 4336] are in the Cuya straddler.  Often 1-3 others\n"
     ]
    }
   ],
   "source": [
    "print(\"FYI for later Cuya work; visualizing Cuya portion of one of the\",len(SCGtriLists),\"good SummCuyaGeau straddlers\")\n",
    "n = random.randint(len(SCGtriLists3))\n",
    "for v in SCGtriLists[n]:\n",
    "    if v in countyTractList[17]:\n",
    "        plotPoly(tractGeom[v])\n",
    "        plotCenter(v,tractGeom[v])\n",
    "#plotPoly(countyGeom[mC])\n",
    "#plotPoly(countyGeom[ppC])\n",
    "#plotPoly(countyGeom[tpC])\n",
    "#plotCenter(n,countyGeom[tpC])\n",
    "plt.show()\n",
    "print(\"regardless of skew ratio, [5036,4333,4387,4348, 4392, 4384, 4332, 4390, 4336] are in the Cuya straddler.  Often 1-3 others\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "3211ee94-e36d-49ce-8f97-ad3cf7f2df8a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, for each optionC Summit straddler piece, divvy remaining Summit into four districts\n",
      "A: the below builds Summit 4-district sets for each Summit triArea.  10 tries / triArea, we store any legit 4-district set\n",
      "For this mC straddler, total 4-district pops for vtd- and muni-snapped should be 496078.0 496187.0\n",
      "0 1 iii,try no. 2-district pops were 248084.0 249330.0 (vtd,muni) vs tgts 248039 248093\n",
      "0 2 iii,try no. 2-district pops were 248266.0 248493.0 (vtd,muni) vs tgts 248039 248093\n",
      "0 3 iii,try no. 2-district pops were 246504.0 247029.0 (vtd,muni) vs tgts 248039 248093\n",
      "0 4 iii,try no. 2-district pops were 248809.0 246510.0 (vtd,muni) vs tgts 248039 248093\n",
      "0 5 iii,try no. 2-district pops were 248850.0 246550.0 (vtd,muni) vs tgts 248039 248093\n",
      "0 6 iii,try no. 2-district pops were 247025.0 246323.0 (vtd,muni) vs tgts 248039 248093\n",
      "0 7 iii,try no. 2-district pops were 248332.0 248947.0 (vtd,muni) vs tgts 248039 248093\n",
      "ABORT for iii,angle deg 0 243 one of the 2-district pieces is discontiguous\n",
      "0 8 iii,try no. 2-district pops were 247373.0 249027.0 (vtd,muni) vs tgts 248039 248093\n",
      "0 9 iii,try no. 2-district pops were 247823.0 246905.0 (vtd,muni) vs tgts 248039 248093\n",
      "7606 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 9 for triDistrict 0\n",
      "0 10 iii,try no. 2-district pops were 248465.0 250169.0 (vtd,muni) vs tgts 248039 248093\n",
      "For this mC straddler, total 4-district pops for vtd- and muni-snapped should be 496013.0 496187.0\n",
      "1 1 iii,try no. 2-district pops were 249298.0 247846.0 (vtd,muni) vs tgts 248006 248093\n",
      "ABORT for iii,angle deg 1 248 one of the 2-district pieces is discontiguous\n",
      "1 2 iii,try no. 2-district pops were 275396.0 249132.0 (vtd,muni) vs tgts 248006 248093\n",
      "ABORT for iii,angle deg= 1 266  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "1 3 iii,try no. 2-district pops were 220.0 247650.0 (vtd,muni) vs tgts 248006 248093\n",
      "ABORT for iii,angle deg= 1 284  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "1 4 iii,try no. 2-district pops were 248218.0 246184.0 (vtd,muni) vs tgts 248006 248093\n",
      "1 5 iii,try no. 2-district pops were 247890.0 249870.0 (vtd,muni) vs tgts 248006 248093\n",
      "7606 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 5 for triDistrict 1\n",
      "1 6 iii,try no. 2-district pops were 248461.0 246066.0 (vtd,muni) vs tgts 248006 248093\n",
      "1 7 iii,try no. 2-district pops were 246950.0 249125.0 (vtd,muni) vs tgts 248006 248093\n",
      "1 8 iii,try no. 2-district pops were 248722.0 245764.0 (vtd,muni) vs tgts 248006 248093\n",
      "ABORT for iii,angle deg= 1 374  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "1 9 iii,try no. 2-district pops were 248285.0 249065.0 (vtd,muni) vs tgts 248006 248093\n",
      "1 10 iii,try no. 2-district pops were 248456.0 250061.0 (vtd,muni) vs tgts 248006 248093\n",
      "For this mC straddler, total 4-district pops for vtd- and muni-snapped should be 496820.0 496187.0\n",
      "2 1 iii,try no. 2-district pops were 247294.0 247851.0 (vtd,muni) vs tgts 248410 248093\n",
      "2 2 iii,try no. 2-district pops were 248303.0 246323.0 (vtd,muni) vs tgts 248410 248093\n",
      "2 3 iii,try no. 2-district pops were 248908.0 249039.0 (vtd,muni) vs tgts 248410 248093\n",
      "ABORT for iii,angle deg 2 240 one of the 2-district pieces is discontiguous\n",
      "2 4 iii,try no. 2-district pops were 248754.0 247172.0 (vtd,muni) vs tgts 248410 248093\n",
      "7606 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 4 for triDistrict 2\n",
      "2 5 iii,try no. 2-district pops were 249607.0 246607.0 (vtd,muni) vs tgts 248410 248093\n",
      "7606 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 5 for triDistrict 2\n",
      "2 6 iii,try no. 2-district pops were 248379.0 250169.0 (vtd,muni) vs tgts 248410 248093\n",
      "2 7 iii,try no. 2-district pops were 248847.0 246731.0 (vtd,muni) vs tgts 248410 248093\n",
      "7606 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 7 for triDistrict 2\n",
      "2 8 iii,try no. 2-district pops were 248613.0 247341.0 (vtd,muni) vs tgts 248410 248093\n",
      "2 9 iii,try no. 2-district pops were 248539.0 247287.0 (vtd,muni) vs tgts 248410 248093\n",
      "2 10 iii,try no. 2-district pops were 248555.0 248609.0 (vtd,muni) vs tgts 248410 248093\n",
      "we drew a TOTAL of 5 legit 4-district Summit sets out of 10 x 3\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, for each optionC Summit straddler piece, divvy remaining Summit into four districts\")\n",
    "#Next for Summit (option B)- chop into 4 districts.  Duplicate the optionA Summit code\n",
    "print(\"A: the below builds Summit 4-district sets for each Summit triArea.  10 tries / triArea, we store any legit 4-district set\")\n",
    "toler = 0.05 * aDP\n",
    "Summ4LISTS, Summ4LISTS3, Summ4triNo = list(), list(), list()  #  \"triNo\" refers to Summ straddler list no from above\n",
    "\n",
    "mC = 76 #Summit from above block\n",
    "for iii, L in enumerate(SummStradLists):\n",
    "    mCpool, mCpool3, muni2dPool3, mCpoolPop, mCpoolPop3 = list(), list(), list(), 0., 0.\n",
    "    for v in countyTractList[mC] :\n",
    "        if v not in L:\n",
    "            mCpool.append(v)\n",
    "            mCpoolPop += tractPop3[v]\n",
    "        if v not in SummStradLists3[iii]:\n",
    "            mCpool3.append(v)\n",
    "            mCpoolPop3 += tractPop3[v]\n",
    "            if tractMuniNo[v] not in muni2dPool3:  #remember, none of the SummCuyaGeau districts had a Summit split\n",
    "                muni2dPool3.append(tractMuniNo[v])  #the munis not in the straddler HD3 - used later in tryNoSplit 2district\n",
    "                \n",
    "    tgt2Pop =  2. * (mCpoolPop )  / int(countyDistricts[mC])  #\"2.\" for 1st of two orthog pizzacuts\n",
    "    tgt2Pop3 = 2. * (mCpoolPop3) / int(countyDistricts[mC])\n",
    "    maxPop, minPop = 2.05*aDP, 1.95*aDP #narrowing these a bit to increase success of later splitting 2 --> 4\n",
    "    tol2 = r3(max(0.02*aDP,min(maxPop - tgt2Pop , tgt2Pop - minPop)) ) #for simplicity, narrow the range so we won't fail on either side ...\n",
    "    tol3 = r3(max(0.02*aDP, min(maxPop - tgt2Pop3, tgt2Pop3- minPop)) ) # ... but force a positive small tolerance\n",
    "    #print(\"for vtdNo\",t,\"targetPops,(tol) for 2-districts w vtdSnap, muniSnap are\",int(tgt2Pop),\"(\",tol2, int(tgt2Pop3),\"(\",tol3,\")\" )\n",
    "    #print(\" ... based on countyPop, snappedHDpop, HD3pop of\", countyPop[mC], mCsnapPop, mCsnapPop3)\n",
    "\n",
    "    print(\"For this mC straddler, total 4-district pops for vtd- and muni-snapped should be\",mCpoolPop, mCpoolPop3)\n",
    "\n",
    "    cutPoint = get_PCP(mCpool, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts   \n",
    "    cutPoint3 = get_PCP(mCpool3, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts       \n",
    "    drawUnsuccessful = True\n",
    "    maxTries = 10 #10 #5 #15\n",
    "    nTries = 0\n",
    "    nInPlayTracts = len(mCpool)\n",
    "    sliceAngle = random.uniform(0., 2.*pi)\n",
    "    while nTries < maxTries: #and drawUnsuccessful == True:  #create the four districts triggered by a random initial cut angle ...\n",
    "        drawUnsuccessful = True #reset here b/c we take any legit 4-district set for each Summit triArea partial\n",
    "        tempLISTS, tempLISTS3 = list(), list() #these will house the 4 district tractLists when both vtd- and muni-drawn 4 are Jiggy\n",
    "        halfList, halfList3, halfPop, halfPop3 = [ [],[] ], [ [],[] ], [0.,0.], [0.,0.]\n",
    "        soFarSoGood = True\n",
    "        nTries +=1  #3/7/23 modification - do NOT specify a starter tract, let cutVTDsnap figure it out\n",
    "        needStarterTract = True\n",
    "        #while needStarterTract:\n",
    "        #    mCcT = mCpool[random.randint(nInPlayTracts)] #... based on a random vtd in these 4 districts for BOTH snapping technqs\n",
    "        #    if mCcT in mCpool3:\n",
    "        #        needStarterTract = False\n",
    "        sliceAngle += math.pi / maxTries  #we methodically try a series of possible cut angles.  (Some will lead to discontinuity)\n",
    "        sliceAngle3 = sliceAngle   #we use same angle for vtd- & muni-snapped\n",
    "        halfList[0], halfPop[0] = cutVtdSnap(tgt2Pop, tol2, tractCP, mCpool, neighborList, tractPop3, sliceAngle, cutPoint)\n",
    "            \n",
    "        halfList3[0], hML, halfPop3[0], splitMuni = cutMuniSnap(tgt2Pop3, tol3, muniTractList,tractCP, mCpool3, muniNeighborList, \n",
    "                                                                tractPop3, muniPop, muniPCP, sliceAngle, cutPoint3)\n",
    "        popGap, fillPop = tgt2Pop3 - halfPop3[0], 0.\n",
    "        if abs(popGap) > tol3 :  #try alternate muni's, do not allow a split at this stage\n",
    "            #print(t,nTries,len(halfList3[0]),halfPop3[0], popGap, int(tol3),\"t,nTries, halfList3[0] len, pop & its popGap, tol3 prior to gFL3nS attempt\")\n",
    "            hT = getFarthestTract(cutPoint3, halfList3[0], tractCP)  #\"home\" tract to bias fill-in toward compactness\n",
    "            vlist, fillPop = getFillList3noSplit(hT, popGap, tol3,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,hML,\n",
    "                                             muni2dPool3, tractCP, neighborList, tractPop3)\n",
    "            halfList3[0] += vlist\n",
    "            halfPop3[0]  += fillPop\n",
    "            #print(\"gFL3nS added pop of\",fillPop,\"from\",len(vlist),\"vtds.  halfList3 len now\",len(halfList3[0]) )\n",
    "        \n",
    "        print(iii,nTries,\"iii,try no. 2-district pops were\",halfPop[0],halfPop3[0],\"(vtd,muni) vs tgts\",int(tgt2Pop), int(tgt2Pop3)) \n",
    "        for v in mCpool:\n",
    "            if v not in halfList[0]:\n",
    "                halfList[1].append(v)\n",
    "                halfPop[1]  += tractPop3[v]\n",
    "        muniPool3 = [list(), list()]  #the list of munis in each half of the 4-district set (we did not allow splitMuni in prev cut)\n",
    "        for v in mCpool3:\n",
    "            if v not in halfList3[0]:\n",
    "                halfList3[1].append(v)\n",
    "                halfPop3[1] += tractPop3[v]\n",
    "            elif tractMuniNo[v] not in muniPool3[0]:\n",
    "                muniPool3[0].append(tractMuniNo[v])\n",
    "        for m in countyMuniList[mC]:\n",
    "            if m not in muniPool3[0] and muniTractList[m][0] not in SummStradLists3[iii]:\n",
    "                muniPool3[1].append(m)\n",
    "        #print(\"and other-slice halfpops were\",halfPop[1], halfPop3[1])\n",
    "        #sIC = 0\n",
    "        #for v in countyTractList[mC]:\n",
    "        #    if v in HDtractList3[t]:\n",
    "        #        sIC +=1\n",
    "        #print(\"nVTDs in half-munisnap0, half-munisnap1, straddler-in-county, total county are\", len(halfList3[0]), len(halfList3[1]),sIC,len(countyTractList[mC]))\n",
    "        for v in countyTractList[mC]:\n",
    "            if v in halfList3[0] and v in halfList3[1]:\n",
    "                raise Exception(v,\"is in both halfLists for\",iii,nTries)\n",
    "            if v in halfList3[1] and v in SummStradLists3[iii]:\n",
    "                raise Exception(v,\"is in 2nd HL3 and straddler for\",iii,nTries)\n",
    "            if v in halfList3[0] and v in SummStradLists3[iii]:\n",
    "                raise Exception(v,\"is in 1st HL3 and straddler for\",iii,nTries)\n",
    "        \n",
    "        if not ( isContiguous(halfList[1],neighborList) and isContiguous(halfList3[1],neighborList) ) :\n",
    "            soFarSoGood = False\n",
    "            print(\"ABORT for iii,angle deg\",iii,int(180./3.1416*sliceAngle),\"one of the 2-district pieces is discontiguous\")\n",
    "            continue  #try a new angle; early discontiguity\n",
    "        if max(abs(halfPop[0]-2*aDP),abs(halfPop[1]-2*aDP),abs(halfPop3[0]-2*aDP), abs(halfPop3[1]-2*aDP) ) > 0.1 * aDP:\n",
    "            soFarSoGood = False\n",
    "            print(\"ABORT for iii,angle deg=\",iii, int(180./3.1416*sliceAngle),\" - our halfPops or halfPop3s are too far from 2*aDP\")\n",
    "            continue #try a new angle; unlikely to hit pop range for qtr districts\n",
    "            \n",
    "        #OK, both 2-district sets are good.  Now try some angles for splitting each of these into \"qtr\" districts\n",
    "        qtrList, qtrList3, qtrPop, qtrPop3 = [list(),list(),list(),list()], [list(),list(),list(),list()], [0.]*4, [0.]*4\n",
    "        nGoodQtrCuts = 0\n",
    "        vtdAngle = [0.]*2\n",
    "        for i, HL in enumerate(halfList):  #first, attempt to generate four good qtr districts for vtd-snap\n",
    "            off2Pop = abs(0.5*halfPop[i] - aDP)\n",
    "            TOL = max( 0.02*aDP, 0.05*aDP - 0.5*off2Pop)  #tighten if 2-district off\n",
    "            qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            qtrCutNo = random.randint(maxQtrCutNo) #we'll make four 45deg-spaced cut tries, but randomize which to try first\n",
    "            \n",
    "            while qLoopNo < maxQtrCutNo: #and haveCutGoodQtr; make all 4 possiblel cuts, keep trying for better\n",
    "                qAngle = sliceAngle + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo #0deg, 45deg, 90deg, 135deg vs orig cut\n",
    "                qElist, qtrPop[qE] = cutVtdSnap(0.5*halfPop[i], TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)  \n",
    "                qtrPop[qO] = halfPop[i] - qtrPop[qE]\n",
    "                qOlist = list()\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop[qE] - aDP) <= 0.05*aDP and abs(qtrPop[qO] - aDP) <= 0.05*aDP :  \n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList[qE], qtrList[qO] = qElist.copy(), qOlist.copy()\n",
    "                            vtdAngle[i] = qAngle #to preserve same angle for 1st try in later munisnap 4-district set\n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        if nGoodQtrCuts < 2:  #we failed to make four contiguous districts for vtd-snap case.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty vtd-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            #plotPoly(countyGeom[mC])\n",
    "            #plotPoly(stradPoly)\n",
    "            #for q in qtrList:\n",
    "            #    if not isContiguous(q,neighborList):\n",
    "            #        for v in q:\n",
    "            #            plotPoly(tractGeom[v])\n",
    "            #plt.show()\n",
    "            continue  #don't bother quartering up the muni-snap\n",
    "            \n",
    "        for i, HL in enumerate(halfList3):  #... and now, 4 qtr-districts for muni-snap\n",
    "            qCutPoint3  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            off2Pop3 = abs(0.5*halfPop3[i] - aDP)\n",
    "            TOL3 = max( 0.02 * aDP, toler - 0.5*off2Pop3)  #tighten if 2-district off\n",
    "            qtrCutNo ,maxQtrCutNo, cutGoodQtr = 0, 4, False\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            qLoopNo = 0\n",
    "            sliceAngle3 = vtdAngle[i]  #of four try angles, we start at the angle that worked for vtdSnap \n",
    "            while qLoopNo < maxQtrCutNo : #and not cutGoodQtr :\n",
    "                qAngle = sliceAngle3 + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo               \n",
    "                #Polly = angledPentaPoly(cutPoint3, qAngle, 5.*tractCP[mCfT].distance(cutPoint3) )\n",
    "                #if Polly.disjoint(tractCP[mCfT]):\n",
    "                #    qAngle += math.pi\n",
    "                qElist, qML, qtrPop3[qE], splitMuni = cutMuniSnap(0.5*halfPop3[i], TOL3, muniTractList,tractCP, HL,\n",
    "                                                                      muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "                \n",
    "                popGap = 0.5*halfPop3[i] - qtrPop3[qE]\n",
    "                list3B, pop3B = list(),0.\n",
    "                if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "                    hT = getFarthestTract(cutPoint3, qElist, tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "                    list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,qML,\n",
    "                                                              muniPool3[i],tractCP, neighborList,tractPop3,0)\n",
    "                qElist, qtrPop3[qE] = qElist + list3B, qtrPop3[qE] + pop3B\n",
    "                qtrPop3[qO] = halfPop3[i] - qtrPop3[qE]\n",
    "                qOlist = list()\n",
    "                #print(\"t,i\",t,i, qtrPop[qE],qtrPop[qO],\"<-qpops, q3pops -->\",qtrPop3[qE],qtrPop3[qO])  #debug\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop3[qE] - aDP) <= 0.05*aDP and abs(qtrPop3[qO] - aDP) <= 0.05*aDP :\n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList3[qE], qtrList3[qO] = qElist.copy(), qOlist.copy()               \n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        #print(\"t,tryNo, final dPops for vtd- and muni\",t,nTries,qtrPop,qtrPop3)\n",
    "        if nGoodQtrCuts < 4:  #we failed to make four contiguous districts for both cases.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty muni-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            continue\n",
    "        \n",
    "        if soFarSoGood:\n",
    "            drawUnsuccessful = False\n",
    "            print(t,\"has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try\",nTries,\"for triDistrict\",iii)\n",
    "            for qD in range(4):\n",
    "                tempLISTS.append(qtrList[qD])\n",
    "                tempLISTS3.append(qtrList3[qD])\n",
    "            Summ4LISTS.append(tempLISTS)\n",
    "            Summ4LISTS3.append(tempLISTS3)\n",
    "            Summ4triNo.append(iii)  #the iii points back to an index in the list of CuyaSumm districts\n",
    "print(\"we drew a TOTAL of\",len(Summ4LISTS),\"legit 4-district Summit sets out of\",maxTries,\"x\",len(SummStradLists) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "92175006-ec16-429e-bcfc-ed629f72c28e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "05-May: flatten the Summit lists, add to the SCG lists, to classify Summits into SCG vs. Summitonly\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"05-May: flatten the Summit lists, add to the SCG lists, to classify Summits into SCG vs. Summitonly\")\n",
    "Alist = SCGtriLists.copy()\n",
    "Blist = list()\n",
    "for plan in Summ4LISTS:\n",
    "    for L in plan:\n",
    "        Blist.append(L)  #individually add the Summit districts from all 4-district plans\n",
    "SummSCGchoosers, SummitChoosers = classifyStraddlers(countyTractList[76], Alist, Blist, tractCP, tractPop3)\n",
    "Alist = SCGtriLists3.copy()\n",
    "Blist = list()\n",
    "for plan in Summ4LISTS3:\n",
    "    for L in plan:\n",
    "        Blist.append(L)  #individually add the Summit districts from all 4-district plans\n",
    "SummSCGchoosers3, SummitChoosers3 = classifyStraddlers(countyTractList[76], Alist, Blist, tractCP, tractPop3)\n",
    "plotPoly(countyGeom[76])\n",
    "for v in SummitChoosers:\n",
    "    plotPoly(tractCP[v].buffer(0.002))\n",
    "for v in SummSCGchoosers:\n",
    "    plotCenter(\"SCG\",tractCP[v])\n",
    "plt.show()\n",
    "plotPoly(countyGeom[76])\n",
    "for v in SummitChoosers3:\n",
    "    plotPoly(tractCP[v].buffer(0.002))\n",
    "for v in SummSCGchoosers3:\n",
    "    plotCenter(\"SCG3\",tractCP[v])\n",
    "plt.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f2e2d051-bf21-4a4d-9b2a-2b067185dc57",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "time for SCG choosers to vote. \n",
      "Voting done.  The most popular district was 42 with 0.239 of the vote\n",
      "Voting done.  The most popular district was 99 with 0.335 of the vote\n",
      "Saving SCG vtd and muni lists with voting to a file\n"
     ]
    }
   ],
   "source": [
    "CuyaSCGers = [5036,4333,4387,4348, 4392, 4384, 4332, 4390, 4336]  #these are SE corner; see above\n",
    "SCGvoters   = SummSCGchoosers + CuyaSCGers + GeauSCGchoosers\n",
    "SCGvoters3 = SummSCGchoosers3 + CuyaSCGers + GeauSCGchoosers3\n",
    "print(\"time for SCG choosers to vote. \" )  \n",
    "planLists = [SCGtriLists, SCGtriLists3]\n",
    "mC = 76  #Summit  \n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "regionalVotingList = [SCGvoters.copy(), SCGvoters3.copy()]\n",
    "reglVTDwts = set1DplanWeights(regionalVotingList[0], planLists[0], tractCP, tractPop3)\n",
    "reglMuniWts = set1DplanWeights(regionalVotingList[1], planLists[1], tractCP, tractPop3)\n",
    "print(\"Saving SCG vtd and muni lists with voting to a file\")\n",
    "#currentRegionNo = 7 #NE\n",
    "date = \"05May\"\n",
    "rLISTS = planLists.copy() \n",
    "rWeights = [reglVTDwts.copy(), reglMuniWts.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, voteFrac = list(), list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "        voteFrac.append(rWeights[i][j])\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":voteFrac,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"SCG\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "b8dc5f88-25c2-4d41-ade7-fa13872be0b3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, non-SCG Summit voters vote on their favorite 4-district plan\n",
      "Voting done.  The most popular plan was 2 with 0.262 of the vote\n",
      "Voting done.  The most popular plan was 2 with 0.332 of the vote\n",
      "let us write the HD weights to a file for sub-region Summit4 NE\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, non-SCG Summit voters vote on their favorite 4-district plan\")\n",
    "SummOnlyVoters, SummOnlyVoters3 = list(), list()\n",
    "mC=76\n",
    "for v in countyTractList[mC]:\n",
    "    if v not in SCGvoters:\n",
    "        SummOnlyVoters.append(v)\n",
    "    if v not in SCGvoters3:\n",
    "        SummOnlyVoters3.append(v)\n",
    "\n",
    "currentRegionNo, fName = 1, \"Summit4\"  \n",
    "areaVoters = [SummOnlyVoters, SummOnlyVoters3] \n",
    "rLISTS = [Summ4LISTS.copy(), Summ4LISTS3.copy() ]\n",
    "rWeights =  [setPlanWeights(areaVoters[0],  rLISTS[0], tractCP, tractPop3), \n",
    "             setPlanWeights(areaVoters[1], rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"let us write the HD weights to a file for sub-region\",fName,regionName[currentRegionNo])\n",
    "date = \"05May\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1cdf9430-36ed-4269-835a-2f2e0078ddb2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the Summit lists to a file, tagging the Summit starter  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the Summit lists to a file, tagging the Summit starter  We will vote later\" )  \n",
    "currentRegionNo = 1  #Lake was separate\n",
    "date = \"05May\"  #note: I reran 5/5/23 and got 5 legit 4-district sets.  The 4/30 run achieved four 4-district sets\n",
    "rLISTS = [Summ4LISTS.copy(), Summ4LISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, starterL = list(), list(), list()\n",
    "    #for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        #dRegionNo.append(currentRegionNo)\n",
    "        #dTL.append(DISTRICT_LIST)\n",
    "\n",
    "    for p,plan in enumerate(PLAN_LISTS):  #Use this block for multi-district plans\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            starterL.append(Summ4triNo[p])\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL,\"Summ4triNo\":starterL} ) \n",
    "    outname = STATE+date+\"Summit4\"+\"_\"+rType[i]+\"C.csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3e53b308-b6cc-4b6c-83a0-f34d6318e7e8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mC, ppC = 77,66  #Trumbull, Portage\n",
    "for v in countyTractList[mC]:\n",
    "    if tractGeom[v].intersects(countyGeom[ppC]):\n",
    "        plotCenter(v,tractGeom[v])\n",
    "for v in countyTractList[ppC]:\n",
    "    if tractGeom[v].intersects(countyGeom[mC]):\n",
    "        plotCenter(v,tractGeom[v])\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyGeom[ppC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a1c3cd6d-ffe4-4963-a885-bfd686173674",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, optionC's Portage-Trumbull combo.  Try cutSnap for both\n",
      "For Portage + Trumbull, our aDP will be 121256 vs usual 119186 of which, Trumbull tgt is 80721.0\n",
      "our target Portage straddler pops are 41469.0 71020.0 for Trum angle -162\n",
      "91114.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -161.99962117441638\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -161.99962117441638\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -143.9996632661479\n",
      "91566.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -143.9996632661479\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -143.9996632661479\n",
      "89679.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -125.9997053578794\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -125.9997053578794\n",
      "93071.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -107.99974744961092\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -107.99974744961092\n",
      "90046.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -89.99978954134244\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -89.99978954134244\n",
      "90015.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -71.99983163307395\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -71.99983163307395\n",
      "87451.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -53.99987372480546\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -53.99987372480546\n",
      "92529.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -35.999915816536976\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -35.999915816536976\n",
      "88093.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -17.999957908268488\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 -17.999957908268488\n",
      "91476.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 0.0\n",
      "151741.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -162 0.0\n",
      "our target Portage straddler pops are 42010.0 66099.0 for Trum angle -144\n",
      "96035.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -161.99962117441638\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -161.99962117441638\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -143.9996632661479\n",
      "96487.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -143.9996632661479\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -143.9996632661479\n",
      "94600.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -125.9997053578794\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -125.9997053578794\n",
      "97992.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -107.99974744961092\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -107.99974744961092\n",
      "94967.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -89.99978954134244\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -89.99978954134244\n",
      "94936.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -71.99983163307395\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -71.99983163307395\n",
      "92372.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -53.99987372480546\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -53.99987372480546\n",
      "97450.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -35.999915816536976\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -35.999915816536976\n",
      "99915.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -17.999957908268488\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 -17.999957908268488\n",
      "96397.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 0.0\n",
      "146820.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -144 0.0\n",
      "our target Portage straddler pops are 40986.0 38661.0 for Trum angle -126\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 -161\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -126 -143.9996632661479\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -126 -143.9996632661479\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 -125\n",
      "125430.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -126 -107.99974744961092\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 -89\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 -71\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 -53\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 -35\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 -17\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -126 0\n",
      "our target Portage straddler pops are 41874.0 38482.0 for Trum angle -108\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -108 -161\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -108 -143.9996632661479\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -108 -125\n",
      "125609.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -108 -107.99974744961092\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -108 -89\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -108 -71\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -108 -53\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -108 -35\n",
      "127532.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -108 -17.999957908268488\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -108 0\n",
      "our target Portage straddler pops are 40379.0 39432.0 for Trum angle -90\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -161\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -90 -143.9996632661479\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -90 -143.9996632661479\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -125\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -107\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -89\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -71\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -53\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -35\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 -17\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -90 0\n",
      "our target Portage straddler pops are 40262.0 40681.0 for Trum angle -72\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -161\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -72 -143.9996632661479\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -72 -143.9996632661479\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -125\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -107\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -89\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -71\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -53\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -35\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 -17\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -72 0\n",
      "our target Portage straddler pops are 40645.0 43834.0 for Trum angle -54\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -161\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -54 -143.9996632661479\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -54 -143.9996632661479\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -125\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -107\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -89\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -71\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -53\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -35\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 -17\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -54 0\n",
      "our target Portage straddler pops are 120714.0 69568.0 for Trum angle -36\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -161.99962117441638\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -161.99962117441638\n",
      "42456.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -161.99962117441638\n",
      "170011.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -161.99962117441638\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -161.99962117441638\n",
      "43468.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -161.99962117441638\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -143.9996632661479\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -143.9996632661479\n",
      "40290.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -143.9996632661479\n",
      "173186.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -143.9996632661479\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -143.9996632661479\n",
      "40293.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -143.9996632661479\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -125.9997053578794\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -125.9997053578794\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -125.9997053578794\n",
      "41757.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -125.9997053578794\n",
      "150548.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -125.9997053578794\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -125.9997053578794\n",
      "62931.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -125.9997053578794\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "40840.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "149536.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "63943.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -107.99974744961092\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -89.99978954134244\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -89.99978954134244\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -89.99978954134244\n",
      "41868.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -89.99978954134244\n",
      "161040.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -89.99978954134244\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -89.99978954134244\n",
      "52439.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -89.99978954134244\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -71.99983163307395\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -71.99983163307395\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -71.99983163307395\n",
      "40960.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -71.99983163307395\n",
      "172620.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -71.99983163307395\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -71.99983163307395\n",
      "40859.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -71.99983163307395\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -53.99987372480546\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -53.99987372480546\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -53.99987372480546\n",
      "42195.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -53.99987372480546\n",
      "172620.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -53.99987372480546\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -53.99987372480546\n",
      "40859.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -53.99987372480546\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -35.999915816536976\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -35.999915816536976\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -35.999915816536976\n",
      "41459.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -35.999915816536976\n",
      "172620.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -35.999915816536976\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -35.999915816536976\n",
      "40859.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -35.999915816536976\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -17.999957908268488\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -17.999957908268488\n",
      "40619.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -17.999957908268488\n",
      "175582.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -17.999957908268488\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -17.999957908268488\n",
      "37897.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 -17.999957908268488\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 0.0\n",
      "201435.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 0.0\n",
      "40248.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 0.0\n",
      "170451.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 0.0\n",
      "150289.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 0.0\n",
      "43028.0 5 offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -36 0.0\n",
      "our target Portage straddler pops are 40520.0 40719.0 for Trum angle -18\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -161\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -18 -143.9996632661479\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are -18 -143.9996632661479\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -125\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -107\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -89\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -71\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -53\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -35\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 -17\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle -18 0\n",
      "our target Portage straddler pops are 40517.0 43186.0 for Trum angle 0\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle 0 -161\n",
      "2 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are 0 -143.9996632661479\n",
      "5 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are 0 -143.9996632661479\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle 0 -125\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle 0 -107\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle 0 -89\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle 0 -71\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle 0 -53\n",
      "GOOD TrumPort 3-district for Tangle, PortAngle 0 -35\n",
      "3 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are 0 -17.999957908268488\n",
      "0 is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are 0 0.0\n",
      "All done creating TrumPort 3-districts.  We had 58 successes out of 100\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, optionC's Portage-Trumbull combo.  Try cutSnap for both\")\n",
    "mC, ppC = 77,66  #Trumbull, Portage\n",
    "TrumPortLISTS, TrumPortLISTS3 = list(), list()\n",
    "PTpop = countyPop[mC] + countyPop[ppC]\n",
    "PTaDP = PTpop / round(PTpop/aDP,0)  #local avg district pop\n",
    "tolr = min(1.05*aDP - PTaDP, PTaDP - 0.95*aDP)\n",
    "tgtMCpop = countyPop[mC]%PTaDP  #do Trumbull first since Warren is problematic to split\n",
    "print(\"For Portage + Trumbull, our aDP will be\",int(PTaDP),\"vs usual\",int(aDP),\"of which, Trumbull tgt is\",tgtMCpop)\n",
    "\n",
    "minAngle, maxAngle, nAngles = -0.9*math.pi, 0., 10  #try full range, expect some rejects\n",
    "TrumAngles = [minAngle + j/(nAngles-1.)*(maxAngle-minAngle) for j in range(nAngles)]\n",
    "PortAngles = TrumAngles.copy()\n",
    "mChT, ppChT = 1993, 5469  #we force-include these two county-line tracts in the straddler district\n",
    "for angle in TrumAngles:  \n",
    "    Tangle = int(180.*angle/math.pi) #for debugging\n",
    "    cutPoint = countyPCP[mC]  \n",
    "    if angledPentaPoly(cutPoint,angle, 5*countyGeom[mC].area**0.5 ).disjoint(tractCP[mChT] ):\n",
    "        angle += math.pi\n",
    "    mClist, mCpop = cutVtdSnap(tgtMCpop, tolr, tractCP, countyTractList[mC], neighborList,tractPop3, angle, cutPoint)\n",
    "    mClist3, mML, mCpop3, sM = cutMuniSnap(tgtMCpop, tolr, muniTractList,tractCP, countyTractList[mC],\n",
    "                                             muniNeighborList,tractPop3, muniPop, muniPCP, angle, cutPoint)\n",
    "    popGap, fillPop = tgtMCpop - mCpop3, 0.\n",
    "    if abs(popGap) > tolr :  #try alternate muni's, do not allow a split at this stage\n",
    "        hT = getFarthestTract(cutPoint, ppClist3, tractCP)   #\"home\" tract to bias fill-in toward compactness\n",
    "        vlist, fillPop = getFillList3noSplit(mChT, popGap, tolr,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                             mML,countyMuniList[mC], tractCP, neighborList, tractPop3)\n",
    "        ppClist3 += vlist\n",
    "        ppCpop3  += fillPop\n",
    "\n",
    "    tgtPPCpop, tgtPPCpop3 = PTaDP - mCpop, PTaDP - mCpop3  #load 'em up!\n",
    "    print(\"our target Portage straddler pops are\",tgtPPCpop, tgtPPCpop3,\"for Trum angle\",Tangle)\n",
    "    for angle in PortAngles:\n",
    "        cutPoint = countyPCP[ppC]\n",
    "        if angledPentaPoly(cutPoint,angle, 5*countyGeom[ppC].area**0.5 ).disjoint(tractCP[ppChT] ):\n",
    "            angle += math.pi\n",
    "        ppClist, ppCpop = cutVtdSnap(tgtPPCpop, tolr, tractCP, countyTractList[ppC], neighborList,tractPop3, angle, cutPoint)\n",
    "        ppClist3, pML, ppCpop3, sM = cutMuniSnap(tgtPPCpop, tolr, muniTractList,tractCP, countyTractList[ppC],\n",
    "                                                 muniNeighborList,tractPop3, muniPop, muniPCP, angle, cutPoint)\n",
    "        popGap, fillPop = tgtPPCpop - ppCpop3, 0.\n",
    "        if abs(popGap) > tolr :  #try alternate muni's, do not allow a split at this stage\n",
    "            hT = getFarthestTract(cutPoint, ppClist3, tractCP)   #\"home\" tract to bias fill-in toward compactness\n",
    "            vlist, fillPop = getFillList3noSplit(hT, popGap, tolr3,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                 pML,countyMuniList[ppC], tractCP, neighborList, tractPop3)\n",
    "            ppClist3 += vlist\n",
    "            ppCpop3  += fillPop\n",
    "            \n",
    "        stradList, stradList3 = mClist + ppClist, mClist3 + ppClist3\n",
    "        stradPop, stradPop3   = mCpop + ppCpop ,    mCpop3 + ppCpop3\n",
    "        TrumList, TrumList3, TrumPop, TrumPop3 = list(), list(), 0., 0.\n",
    "        for v in countyTractList[mC]:\n",
    "            if v not in stradList:\n",
    "                TrumList.append(v)\n",
    "                TrumPop += tractPop3[v]\n",
    "            if v not in stradList3:\n",
    "                TrumList3.append(v)\n",
    "                TrumPop3 += tractPop3[v]        \n",
    "        PortList, PortList3, PortPop, PortPop3 = list(), list(), 0., 0.\n",
    "        for v in countyTractList[ppC]:\n",
    "            if v not in stradList:\n",
    "                PortList.append(v)\n",
    "                PortPop += tractPop3[v]\n",
    "            if v not in stradList3:\n",
    "                PortList3.append(v)\n",
    "                PortPop3 += tractPop3[v]\n",
    "        \n",
    "        isJiggy = True\n",
    "        currentLists = [stradList, TrumList, PortList, stradList3, TrumList3, PortList3]\n",
    "        currentPops = [stradPop, TrumPop, PortPop, stradPop3, TrumPop3, PortPop3]\n",
    "        for jjj,L in enumerate(currentLists):\n",
    "            if not isContiguous(L, neighborList):\n",
    "                isJiggy = False\n",
    "                print(jjj,\"is discontiguous. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are\",Tangle,180./3.1416*angle)\n",
    "        for p in currentPops :\n",
    "            if abs(p - aDP) > 0.05*aDP:\n",
    "                isJiggy = False\n",
    "                print(p,jjj,\"offtgt Pop. (0,1,2,3,4,5 = strad-Trum-Port, strad-Trum-Port3).Angles are\",Tangle,180./3.1416*angle)\n",
    "        if isJiggy:\n",
    "            TrumPortLISTS.append([stradList,TrumList, PortList])\n",
    "            TrumPortLISTS3.append([stradList3, TrumList3, PortList3])\n",
    "            print(\"GOOD TrumPort 3-district for Tangle, PortAngle\",Tangle,int(180./3.1416*angle) )\n",
    "            \n",
    "print(\"All done creating TrumPort 3-districts.  We had\",len(TrumPortLISTS),\"successes out of\",len(TrumAngles)*len(PortAngles) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "259c6c02-e24f-4766-8d51-8bf1efd997de",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the TrumPort lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the TrumPort lists to a file.  We will vote later\" )  \n",
    "currentRegionNo = 1  #Lake was separate\n",
    "date = \"30Apr\"\n",
    "rLISTS = [TrumPortLISTS.copy(), TrumPortLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, starterL = list(), list(), list()\n",
    "    #for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        #dRegionNo.append(currentRegionNo)\n",
    "        #dTL.append(DISTRICT_LIST)\n",
    "\n",
    "    for p,plan in enumerate(PLAN_LISTS):  #Use this block for multi-district plans\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            #starterL.append(Summ4triNo[p])\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL} ) #,\"Summ4triNo\":starterL} ) \n",
    "    outname = STATE+date+\"TrumPort\"+\"_\"+rType[i]+\"C.csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "40b04120-54dc-4fb3-884a-545e12e48d98",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now TrumPort voting\n",
      "Voting done.  The most popular plan was 0 with 0.273 of the vote\n",
      "Voting done.  The most popular plan was 0 with 0.26 of the vote\n",
      "let us write the HD weights to a file for sub-region TrumPort SE\n"
     ]
    }
   ],
   "source": [
    "print(\"now TrumPort voting\")\n",
    "currentRegionNo, fName = 1, \"TrumPort\"\n",
    "areaVoters = countyTractList[66] + countyTractList[77]\n",
    "rLISTS = [TrumPortLISTS.copy(), TrumPortLISTS3.copy() ]\n",
    "rWeights =  [setPlanWeights(areaVoters,  rLISTS[0], tractCP, tractPop3), setPlanWeights(areaVoters, rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"let us write the HD weights to a file for sub-region\",fName,regionName[currentRegionNo])\n",
    "date = \"30April\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2478bc55-9122-4253-9ca0-7ca27c93a62f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing Trum-Port 3-district area; there are 58 to choose from\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing Trum-Port 3-district area; there are\",len(TrumPortLISTS),\"to choose from\")\n",
    "n = random.randint(len(TrumPortLISTS))\n",
    "for v in TrumPortLISTS3[n][0]:\n",
    "    plotPoly(tractGeom[v])\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyGeom[ppC])\n",
    "plotCenter(n,countyGeom[ppC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "712d5813-e404-4735-a7fb-86f156cb7360",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mC = 75 #Stark\n",
    "swellStarters = [7855,7815,7732]\n",
    "for v in countyTractList[mC]:\n",
    "    if tractGeom[v].intersects(countyGeom[mC].exterior):\n",
    "        plotPoly(tractGeom[v])\n",
    "        if v in swellStarters:\n",
    "            plotCenter(v,tractGeom[v])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "5622edba-d144-4cb4-88dc-8e765e6a3e07",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7855, 7815, 7732]\n"
     ]
    }
   ],
   "source": [
    "print(swellStarters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "97e3dc50-16e8-419b-8ed5-458aa90ce909",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OptionC Stark. Can pair to south and east with minimal constraint.  Straddler district will be RED.\n",
      "Therefore, ignore straddler, focus on possible trisects after clipping straddler.\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 352753.0 350620.0 117584 116873 3646\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 0.5 212.142\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 0.5 235.285\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 0.5 258.428\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 0.5 281.571\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7855 75 with angle 303.141 we are short by 21243.33333333333\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 0.5 304.714\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 0.5 327.856\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 0.5 350.999\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 352753.0 350620.0 117584 116873 3646\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 1.0 212.142\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 1.0 235.285\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 1.0 258.428\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 1.0 281.571\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7855 75 with angle 303.141 we are short by 21243.33333333333\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 1.0 304.714\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 1.0 327.856\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 1.0 350.999\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 352753.0 350620.0 117584 116873 3646\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 2.0 212.142\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 2.0 235.285\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 2.0 258.428\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 2.0 281.571\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7855 75 with angle 303.141 we are short by 21243.33333333333\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 2.0 304.714\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 2.0 327.856\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7855 2.0 350.999\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 352916.0 352584.0 117638 117528 4300\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 188.024 we are short by 113085.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 211.048 we are short by 67663.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 234.071 we are short by 63386.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 257.094 we are short by 35397.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 280.118 we are short by 5073.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 303.141 we are short by 5073.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 326.165 we are short by 5073.0\n",
      "try muniswell instead\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 352916.0 351867.0 117638 117289 4061\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 188.024 we are short by 17661.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 211.048 we are short by 90742.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 234.071 we are short by 67590.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 257.094 we are short by 39601.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 280.118 we are short by 9277.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 303.141 we are short by 9277.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 326.165 we are short by 9277.0\n",
      "try muniswell instead\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 352916.0 351867.0 117638 117289 4061\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 188.024 we are short by 17661.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 211.048 we are short by 90742.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 234.071 we are short by 67590.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 257.094 we are short by 39601.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 280.118 we are short by 9277.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 303.141 we are short by 9277.0\n",
      "try muniswell instead\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7815 75 with angle 326.165 we are short by 9277.0\n",
      "try muniswell instead\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 352752.0 351867.0 117584 117289 4061\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7732 75 with angle 188.024 we are short by 17661.0\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 189.0\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7732 75 with angle 211.048 we are short by 90742.0\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 212.142\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7732 75 with angle 234.071 we are short by 67590.0\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 235.285\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7732 75 with angle 257.094 we are short by 39601.0\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 258.428\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7732 75 with angle 280.118 we are short by 9277.0\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 281.571\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7732 75 with angle 303.141 we are short by 9277.0\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 304.714\n",
      "uh oh, FAILED to muni-snap 0thD for t,mC 7732 75 with angle 326.165 we are short by 9277.0\n",
      "try muniswell instead\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 327.856\n",
      "SUCCESS for swellStarter, skewRatio, NEcutAngle 7732 0.5 350.999\n",
      "the Stark partial leaves a discontinuous remnant for t,xyR = 7732 1.0 .  STOP\n",
      "the Stark partial leaves a discontinuous remnant for t,xyR = 7732 2.0 .  STOP\n",
      "all done attempting 3 Stark schemeB districts.  We had a total of 29 successes and 43 fails\n"
     ]
    }
   ],
   "source": [
    "print(\"OptionC Stark. Can pair to south and east with minimal constraint.  Straddler district will be RED.\")\n",
    "print(\"Therefore, ignore straddler, focus on possible trisects after clipping straddler.\")\n",
    "mC = 75 #Stark\n",
    "SaDP = 0.98*aDP  #shade down as we had to shade up in NE Ohio\n",
    "tol = min(1.05*aDP-SaDP, SaDP - 0.95*aDP)\n",
    "finalStarkLISTS, finalStarkLISTS3, nAngles = list(), list(), 8\n",
    "nStarkFail, nStarkGood, minAngle, maxAngle = 0,0, 0.05*math.pi, 0.95*math.pi #0., float(nAngles-1.)/nAngles * 2.*math.pi\n",
    "# had initially tried a wide range of angles, but many fail due to Canton.  Go back to scheme B's narrow range of angles\n",
    "skewRatio = [0.5, 1.0, 2.0]\n",
    "targetMCP = countyPop[mC]%SaDP\n",
    "for iii, t in enumerate(swellStarters):\n",
    "    homeM = tractMuniNo[t]\n",
    "    for xyR in skewRatio:\n",
    "        mClist, mCpop = vtdSwell(tractCP[t], targetMCP, tol, tractCP, countyTractList[mC],neighborList,tractPop3,mCxyR)\n",
    "        mClist3, mCpop3, totSplits = muniSwell(tractCP[t], targetMCP, tol, aDP, muniPop, muniPCP, muniTractList, \n",
    "                                               muniNeighborList,countyMuniList[mC], tractCP, neighborList, tractPop3, 1, xyR)\n",
    "        remnList, remnList3, remnPop, remnPop3 = list(), list(), 0., 0.\n",
    "        cMLremn = list()  #the county's muni's in the \"remnant\" after removing the straddler piece\n",
    "        for v in countyTractList[mC]:\n",
    "            if v not in mClist:\n",
    "                remnList.append(v)\n",
    "                remnPop += tractPop3[v]\n",
    "            if v not in mClist3:\n",
    "                remnList3.append(v)\n",
    "                remnPop3 += tractPop3[v]\n",
    "                if tractMuniNo[v] not in cMLremn:\n",
    "                    cMLremn.append(tractMuniNo[v])\n",
    "        if not (isContiguous(remnList,neighborList) and isContiguous(remnList3, neighborList)):\n",
    "            print(\"the Stark partial leaves a discontinuous remnant for t,xyR =\",t,xyR,\".  STOP\")\n",
    "            nStarkFail += nAngles\n",
    "            continue  #no hope of forming three districts\n",
    "        CaDP =  remnPop  / int(countyPop[mC]/aDP)  #shade targets and tolerances to maximize chance of all three good districts\n",
    "        CaDP3 = remnPop3 / int(countyPop[mC]/aDP)\n",
    "        Ctoler, Ctoler3 = min(CaDP - 0.95*aDP, 1.05*aDP - CaDP) , min(CaDP3 - 0.95*aDP, 1.05*aDP - CaDP3)\n",
    "        print(\"our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are\",remnPop, remnPop3, int(CaDP), int(CaDP3), int(Ctoler3) )\n",
    "        for angleNo in range(nAngles):\n",
    "            angle = minAngle + (maxAngle - minAngle) * angleNo/ float(nAngles - 1.)\n",
    "            NEcorner = getClosestTract(Point(-81.08, 40.95), remnList, tractCP)  #ID a Stark NE corner tract to force it into Stark D1\n",
    "            if angledPentaPoly(countyPCP[c], angle, 5.*countyGeom[c].area**0.5 ).disjoint(tractCP[NEcorner]) :\n",
    "                angle += math.pi\n",
    "            #print(\"now trying angle\",angleNo,\"of\",nAngles,\"angle deg =\",r3(angle*180./3.1416),\"for swellStarterNo\",iii )\n",
    "            StarkLISTS, StarkPops   = [list(), list(), list()], [0., 0., 0.]\n",
    "            StarkLISTS3, StarkPops3 = [list(), list(), list()], [0., 0., 0.]\n",
    "            StarkLISTS[0], StarkPops[0] = cutVtdSnap(CaDP, Ctoler, tractCP, remnList,neighborList,tractPop3,angle, countyPCP[mC])\n",
    "            list3, cML3, pop3, splitMuni = cutMuniSnap(CaDP3, Ctoler3, muniTractList, tractCP, remnList3, muniNeighborList,tractPop3,\n",
    "                                                       muniPop, muniPCP, angle, countyPCP[mC])\n",
    "            #print(t,\"=t. cutMuniSnap yielded a pop of\",pop3,\"vs target\",CaDP,\"for angle\",r3(angle*180./3.1416))\n",
    "            popGap = CaDP3 - pop3\n",
    "            list3B, pop3B = list(), 0.\n",
    "            if (abs(popGap) > Ctoler3) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "                if popGap < 0  :\n",
    "                    raise Exception(\"error! pop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "                #cMLremn = countyMuniList[c].copy()  #see 20 lines above              \n",
    "                fT = getFarthestTract(countyPCP[mC], list3, tractCP)   #\"home\" tract to bias fill-in toward compactness\n",
    "                list3B, pop3B = getFillList3noSplit(fT, popGap, Ctoler3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                    cML3, cMLremn, tractCP, neighborList,tractPop3)\n",
    "                #not allowing a split to reserve for final 2-district split\n",
    "                if pop3B == 0. :\n",
    "                    print(\"uh oh, FAILED to muni-snap 0thD for t,mC\",t,mC,\"with angle\",r3(180//3.1416*angle),\"we are short by\",popGap)\n",
    "                    print(\"try muniswell instead\")\n",
    "                    pop3B = 0.5\n",
    "            if pop3B == 0.5:  #try swelling instead\n",
    "                #homeM = tractMuniNo[t]  #already assigned\n",
    "                mCxyR = 2.**((angle - 0.5*(minAngle+maxAngle))/(0.5*(maxAngle-minAngle)) ) #add var'n in skew angle\n",
    "                cTcP = tractCP[t]\n",
    "                list3B, pop3B = list(),0\n",
    "                StarkLISTS[0], StarkPops[0] = vtdSwell(cTcP, CaDP, Ctoler, tractCP, remnList,neighborList,tractPop3,mCxyR)   \n",
    "                StarkLISTS3[0], StarkPops3[0], totSplits = muniSwell(cTcP, CaDP3, Ctoler3, aDP, muniPop, muniPCP, muniTractList,\n",
    "                                                       muniNeighborList, cMLremn, tractCP, neighborList, tractPop3, 1, mCxyR)                \n",
    "                #nStarkFail +=1\n",
    "                #print(\"stop for t,skew,angle\",t,xyR,angle)\n",
    "                #continue\n",
    "            #print(\"continuing for t,skew,angle\",t,xyR,angle)\n",
    "            else:\n",
    "                StarkLISTS3[0], StarkPops3[0] = list3 + list3B, pop3 + pop3B  #this is the 0th Stark whole district.\n",
    "            if not (isContiguous(StarkLISTS[0],neighborList) and isContiguous(StarkLISTS3[0],neighborList)) :\n",
    "                #print(r3(180./math.pi),\"deg cut for Stark0 created a discontiguous vtd- or muni NE Stark district.  Give up on this HD\")\n",
    "                nStarkFail +=1\n",
    "                continue          \n",
    "            #below - Now bisect remainder.  modified from Lorain\n",
    "            doubleList, doubleList3, doublePop, doublePop3 = list(), list(), 0., 0.\n",
    "            for v in remnList:\n",
    "                if v not in StarkLISTS[0]:\n",
    "                    doubleList.append(v)\n",
    "                    doublePop  += tractPop3[v]\n",
    "            muniPool3 = list()  #the list of munis in remaining 2-district set (we did not allow splitMuni in prev cut)\n",
    "            for v in remnList3:\n",
    "                if v not in StarkLISTS3[0] :\n",
    "                    doubleList3.append(v)\n",
    "                    doublePop3 += tractPop3[v]\n",
    "                    if tractMuniNo[v] not in muniPool3:  #Stark district0 do not bequeath any split muni's to d1 or d2\n",
    "                        muniPool3.append(tractMuniNo[v])\n",
    "            if not (isContiguous(doubleList,neighborList) and isContiguous(doubleList3,neighborList)) :\n",
    "                #print(r3(180./math.pi),\"deg cut for Stark0 left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\")\n",
    "                nStarkFail +=1\n",
    "                continue\n",
    "\n",
    "            HL = doubleList.copy()  #reusing Dayton nomenclature\n",
    "            off2Pop = abs(0.5*doublePop - aDP)\n",
    "            TOL = max( 0.02*aDP, 0.05*aDP - 0.5*off2Pop)  #tighten if 2-district off\n",
    "            qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "            minAngle2, maxAngle2 = 0., math.pi   \n",
    "            qAngle0 = random.uniform(minAngle2, maxAngle2)  #any cut may work\n",
    "\n",
    "            bestDlists = [list(), list(), list(), list()]\n",
    "            nAngleTries,lowScore = 10, 999999.\n",
    "            qLoopNo = 0\n",
    "            foundGoodAngle = False\n",
    "            while qLoopNo < nAngleTries:  #try multiple angles, save the districts with lowest score if all districts legit\n",
    "                qLoopNo +=1\n",
    "                qAngle = qAngle0 + math.pi * float(qLoopNo)/nAngleTries\n",
    "                #print(\"trying angle\",qAngle*180./3.1416,\"for 1st cut angle\",angle*180./3.1416)\n",
    "                dblList, dblPop = [list(), list()], [0., 0.]\n",
    "                dblList[0], dblPop[0] = cutVtdSnap(0.5*doublePop, TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)\n",
    "                for tt in HL:\n",
    "                    if tt not in dblList[0]:  #throw all remaining vtds into 2nd whole Lorain district\n",
    "                        dblList[1].append(tt)\n",
    "                dblPop[1] = doublePop - dblPop[0]\n",
    "\n",
    "                HL3 = doubleList3.copy()  #reusing Dayton code\n",
    "                TOL3 = min(1.05*aDP - 0.5*doublePop3, 0.5*doublePop3 - 0.95*aDP)\n",
    "                dblList3, dblPop3 = [list(), list()], [0., 0.]\n",
    "                qCutPoint3 = get_PCP(HL3, tractCP, tractPop3)  #note: reusing same angle as vtd-snapped (horiz,diag or vert)\n",
    "                dblList3[0], dML, dblPop3[0], splitMuni = cutMuniSnap(0.5*doublePop3, TOL3, muniTractList,tractCP, HL3,\n",
    "                                                                      muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "                popGap = 0.5*doublePop3 - dblPop3[0]\n",
    "                list3B, pop3B = list(),0.\n",
    "                if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "                    hT = getFarthestTract(qCutPoint3, dblList3[0], tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "                    list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,dML,\n",
    "                                                              muniPool3,tractCP, neighborList,tractPop3,0)\n",
    "                dblList3[0], dblPop3[0] = dblList3[0] + list3B, dblPop3[0] + pop3B\n",
    "                for tt in HL3:\n",
    "                    if tt not in dblList3[0]:  #throw all remaining vtds into 3rd whole Lucas district\n",
    "                        dblList3[1].append(tt)\n",
    "                dblPop3[1] = doublePop3 - dblPop3[0]\n",
    "                isJiggy = True\n",
    "                score = 0.\n",
    "                currentLists,currentPops = [dblList[0], dblList[1], dblList3[0], dblList3[1]] ,[dblPop[0],dblPop[1], dblPop3[0], dblPop3[1] ]\n",
    "                for L in currentLists:\n",
    "                    score += compactScore(L,tractCP, tractPop3)\n",
    "                    if not isContiguous(L, neighborList):\n",
    "                        isJiggy = False\n",
    "                for p in currentPops :\n",
    "                    if abs(p - aDP) > toler:\n",
    "                        isJiggy = False\n",
    "                if isJiggy and score < lowScore:\n",
    "                    foundGoodAngle = True\n",
    "                    lowScore = score\n",
    "                    bestDlists = currentLists.copy()\n",
    "                    bestDpops =  currentPops.copy()\n",
    "                    bestAngle = qAngle\n",
    "\n",
    "            if foundGoodAngle:  #overwrite.  If we didn't overwrite, we'll catch the fails in the nGood block\n",
    "                dblList[0], dblList[1], dblPop[0], dblPop[1] = bestDlists[0], bestDlists[1], bestDpops[0], bestDpops[1]\n",
    "                dblList3[0], dblList3[1], dblPop3[0], dblPop3[1] = bestDlists[2], bestDlists[3], bestDpops[2], bestDpops[3]\n",
    "            for i, L in enumerate(dblList3):\n",
    "                StarkLISTS[i+1] = dblList[i]\n",
    "                StarkLISTS3[i+1] = dblList3[i]\n",
    "                StarkPops[i+1] = dblPop[i]\n",
    "                StarkPops3[i+1] = dblPop3[i]\n",
    "            nGood, nGood3 = 0,0\n",
    "            for i, L in enumerate(StarkLISTS):\n",
    "                if abs(StarkPops[i] - aDP) <= 0.05 * aDP and isContiguous(StarkLISTS[i],neighborList):\n",
    "                    nGood +=1\n",
    "                else:\n",
    "                    hi=\"hi\"\n",
    "                    #print(t,i,StarkPops[i],isContiguous(StarkLISTS[i],neighborList),\"t,i,vtd-pop,contiguity = FAIL\")\n",
    "                if abs(StarkPops3[i] - aDP) <= 0.05 * aDP and isContiguous(StarkLISTS3[i],neighborList):\n",
    "                    nGood3 +=1\n",
    "                else:\n",
    "                    hi=\"hi\"\n",
    "                    #print(t,i,StarkPops3[i],isContiguous(StarkLISTS3[i],neighborList),\"t,i,muni-pop,contiguity = FAIL\")\n",
    "\n",
    "            if nGood == 3 and nGood3 == 3:\n",
    "                print(\"SUCCESS for swellStarter, skewRatio, NEcutAngle\",t,xyR, r3(angle*180./3.1416) )\n",
    "                nStarkGood +=1\n",
    "                finalStarkLISTS.append( [StarkLISTS[0],  StarkLISTS[1],   StarkLISTS[2] ] )\n",
    "                finalStarkLISTS3.append([StarkLISTS3[0], StarkLISTS3[1],  StarkLISTS3[2] ] )\n",
    "            else:\n",
    "                nStarkFail +=1\n",
    "print(\"all done attempting 3 Stark schemeB districts.  We had a total of\",nStarkGood,\"successes and\",nStarkFail,\"fails\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "32ccbd17-d3dd-43de-bbe5-f92c079f5314",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29\n",
      "let us write the Stark lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(len(finalStarkLISTS))\n",
    "print(\"let us write the Stark lists to a file.  We will vote later\" )  \n",
    "currentRegionNo = 1  #Lake was separate\n",
    "date = \"30Apr\"\n",
    "rLISTS = [finalStarkLISTS.copy(), finalStarkLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo, starterL = list(), list(), list()\n",
    "    #for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        #dRegionNo.append(currentRegionNo)\n",
    "        #dTL.append(DISTRICT_LIST)\n",
    "\n",
    "    for p,plan in enumerate(PLAN_LISTS):  #Use this block for multi-district plans\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            #starterL.append(Summ4triNo[p])\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL} ) #,\"Summ4triNo\":starterL} ) \n",
    "    outname = STATE+date+\"Stark3\"+\"_\"+rType[i]+\"C.csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "8c15d724-d459-483d-84b7-eb5ddb6efd37",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now Stark with voting\n",
      "Voting done.  The most popular plan was 28 with 0.139 of the vote\n",
      "WARNING! vtd 7733 not found in any of 29 regional plans\n",
      "WARNING! vtd 7814 not found in any of 29 regional plans\n",
      "WARNING! vtd 7815 not found in any of 29 regional plans\n",
      "WARNING! vtd 7877 not found in any of 29 regional plans\n",
      "WARNING! vtd 8325 not found in any of 29 regional plans\n",
      "Voting done.  The most popular plan was 28 with 0.287 of the vote\n",
      "let us write the HD weights to a file for region SE\n"
     ]
    }
   ],
   "source": [
    "print(\"now Stark with voting\")\n",
    "currentRegionNo, fName = 7, \"Stark\"\n",
    "areaVoters = countyTractList[75]\n",
    "rLISTS = [finalStarkLISTS.copy(), finalStarkLISTS3.copy() ]\n",
    "rWeights =  [setPlanWeights(areaVoters,  rLISTS[0], tractCP, tractPop3), setPlanWeights(areaVoters, rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"let us write the HD weights to a file for region\",regionName[currentRegionNo])\n",
    "date = \"30April\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "702f5a5b-5d65-465e-a6ba-4ee297c0e4ee",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotPoly(countyGeom[75])\n",
    "plotPoly(tractGeom[7733])\n",
    "plotPoly(tractGeom[7814])\n",
    "plotPoly(tractGeom[7815])\n",
    "plotPoly(tractGeom[7877])\n",
    "plotPoly(tractGeom[8325])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "924dc2c7-a759-4bf8-92e2-d00e358201b7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing Stark 3-district area; there are 29 to choose from\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing Stark 3-district area; there are\",len(finalStarkLISTS3),\"to choose from\")\n",
    "n = random.randint(len(finalStarkLISTS3))\n",
    "for v in finalStarkLISTS3[n][0]:\n",
    "    plotPoly(tractGeom[v])\n",
    "for v in finalStarkLISTS3[n][2]:\n",
    "    plotPoly(tractCP[v].buffer(0.005))\n",
    "for v in finalStarkLISTS3[n][1]:\n",
    "    plotPoly(tractCP[v].buffer(0.002))\n",
    "plotPoly(countyGeom[75])\n",
    "plotCenter(n,countyGeom[75])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "a1d36fd5-83c2-407f-ad66-73cebec8ffa2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "tractGEOID = tractPopFile['GEOID20'].to_list()  #needed to translate DRA-based precinct assignment files\n",
    "Cuya10LISTS = list()\n",
    "Cuya10LISTS3 = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "54c7a9e9-7e98-470e-a4e2-af7e8802fb52",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, pull in DRA-drawn precinct assignment file options for Cuyahoga, create their vtd lists\n",
      "[19, 21, 16, 17, 13, 18, 15, 22, 14, 20, 23]\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, pull in DRA-drawn precinct assignment file options for Cuyahoga, create their vtd lists\")\n",
    "planFilename = \"CuyaCompactCmuni_prct.csv\"  #FYI, there are A,B,C plans for vtd- and muni-snapped\n",
    "planDF = pd.read_csv(\"state_HD_output/CuyaCompactPrecinctAFs/\"+planFilename)\n",
    "planGEOID = planDF[\"GEOID20\"]\n",
    "planDno = planDF[\"District\"]\n",
    "DnosInPlan = list()  #list of all district numbers in plan\n",
    "for Dno in planDno:\n",
    "    if Dno not in DnosInPlan:\n",
    "        DnosInPlan.append(Dno)\n",
    "print(DnosInPlan)\n",
    "planDlist = [list() for i in range(np.max(DnosInPlan)+1) ]  #district #23 is a partial, but save it for now\n",
    "for i, geoid in enumerate(planGEOID):\n",
    "    planDlist[planDno[i]].append(tractGEOID.index(geoid))\n",
    "newPlanList = list()\n",
    "for p in range(13,23,1):\n",
    "    newPlanList.append(planDlist[p])  #skip the district 23 partial -- we cover this in our SCG lists\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "c2aa923e-530d-4da9-ba1b-1a75c3545b8b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "style = \"muni\"  #\"muni\"\n",
    "if style == \"vtd\":   #done so far: Cvtd, \n",
    "    Cuya10LISTS.append(newPlanList)\n",
    "if style == \"muni\":\n",
    "    Cuya10LISTS3.append(newPlanList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "979bccf0-9885-4180-9d92-46c19c9bd980",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is one of those DRA-drawn districts\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is one of those DRA-drawn districts\")\n",
    "d = 16  #any number 13 - 22\n",
    "for v in newPlanList[d-13]:  \n",
    "    plotPoly(tractGeom[v])\n",
    "plotPoly(countyGeom[17])\n",
    "plotCenter(d, countyGeom[17])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "c7d97bec-05f9-4f3c-b444-8563a2a6ff50",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, Cuyahoga vote, except for the Solon 9-vtd corner firmly in SCG\n",
      "Voting done.  The most popular plan was 2 with 0.411 of the vote\n",
      "WARNING! vtd 4334 not found in any of 3 regional plans\n",
      "Voting done.  The most popular plan was 0 with 0.433 of the vote\n",
      "let us write the HD weights to a file for sub-region Cuya10 NE\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, Cuyahoga vote, except for the Solon 9-vtd corner firmly in SCG\")\n",
    "CuyaVoters = list()\n",
    "for v in countyTractList[17]:\n",
    "    if v not in [5036,4333,4387,4348, 4392, 4384, 4332, 4390, 4336]:\n",
    "        CuyaVoters.append(v)\n",
    "currentRegionNo, fName = 1, \"Cuya10\"\n",
    "areaVoters = CuyaVoters.copy()\n",
    "rLISTS = [Cuya10LISTS.copy(), Cuya10LISTS3.copy() ]\n",
    "rWeights =  [setPlanWeights(areaVoters,  rLISTS[0], tractCP, tractPop3), setPlanWeights(areaVoters, rLISTS[1], tractCP, tractPop3) ]\n",
    "\n",
    "print(\"let us write the HD weights to a file for sub-region\",fName,regionName[currentRegionNo])\n",
    "date = \"07May\"\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDwt\":dWeight,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"_\"+fName+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84e55e3b-ae0a-429a-898e-ff3dad6d0090",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "print(\"For Franklin 24 and Hamilton 30, we will Excel copy-paste HDwt's and HDTL's from prev files...\")\n",
    "print(\"... into standalone files.  For vtd-snapped, the source file is OH99HD1wholeCounty20Jan.csv\")\n",
    "print(\"For muni, the source file is OH99wholeCountyMuniSnap21Jan.csv.  All these are single Home Districts\")\n",
    "print(\"These were made in OH_legisl_byCounty and OH_patch_legisl_org, respectively\") \n",
    "print(\"We need to multiply their HDwt's by 99 to get total HDwt of 11.11 and 7.000 for each county, respectively\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "132aeb6a-b5c4-409c-9b55-630a15f2b8ca",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now pull all HDwts into a master file\n",
      "We read in the tractLists and weights for OH04May_Mahoning_vtd\n",
      "We read in the tractLists and weights for OH05May_Dayton_vtd\n",
      "We read in the tractLists and weights for OH05May_Summit4_vtd\n",
      "We read in the tractLists and weights for OH05MayAshtGeauC_vtd\n",
      "We read in the tractLists and weights for OH05MaySCG_vtd\n",
      "We read in the tractLists and weights for OH06May_Toledo_vtd\n",
      "We read in the tractLists and weights for OH07May_Cuya10_vtd\n",
      "We read in the tractLists and weights for OH19Mar_Delaware_vtd\n",
      "We read in the tractLists and weights for OH19MarREDO_N-Lorain_vtd\n",
      "We read in the tractLists and weights for OH21Jan_Franklin_vtd\n",
      "We read in the tractLists and weights for OH21Jan_Hamilton_vtd\n",
      "We read in the tractLists and weights for OH30AprAthens_vtd\n",
      "We read in the tractLists and weights for OH30April_Lake_vtd\n",
      "We read in the tractLists and weights for OH30April_Stark_vtd\n",
      "We read in the tractLists and weights for OH30April_TrumPort_vtd\n",
      "The sum of HDwts for vtd is 62.106\n",
      "We read in the tractLists and weights for OH04May_Mahoning_muni\n",
      "We read in the tractLists and weights for OH05May_Dayton_muni\n",
      "We read in the tractLists and weights for OH05May_Summit4_muni\n",
      "We read in the tractLists and weights for OH05MayAshtGeauC_muni\n",
      "We read in the tractLists and weights for OH05MaySCG_muni\n",
      "We read in the tractLists and weights for OH06May_Toledo_muni\n",
      "We read in the tractLists and weights for OH07May_Cuya10_muni\n",
      "We read in the tractLists and weights for OH19Mar_Delaware_muni\n",
      "We read in the tractLists and weights for OH19MarREDO_N-Lorain_muni\n",
      "We read in the tractLists and weights for OH21Jan_Franklin_muni\n",
      "We read in the tractLists and weights for OH21Jan_Hamilton_muni\n",
      "We read in the tractLists and weights for OH30AprAthens_muni\n",
      "We read in the tractLists and weights for OH30April_Lake_muni\n",
      "We read in the tractLists and weights for OH30April_Stark_muni\n",
      "We read in the tractLists and weights for OH30April_TrumPort_muni\n",
      "The sum of HDwts for muni is 62.106\n"
     ]
    }
   ],
   "source": [
    "print(\"Now pull all HDwts into a master file\")\n",
    "filenames = [\"OH04May_Mahoning_\" ,\"OH05May_Dayton_\" ,\"OH05May_Summit4_\",\"OH05MayAshtGeauC_\",\"OH05MaySCG_\",\n",
    "               \"OH06May_Toledo_\",\"OH07May_Cuya10_\",\"OH19Mar_Delaware_\",\"OH19MarREDO_N-Lorain_\",\n",
    "               \"OH21Jan_Franklin_\",\"OH21Jan_Hamilton_\",\"OH30AprAthens_\",\n",
    "               \"OH30April_Lake_\",\"OH30April_Stark_\",\"OH30April_TrumPort_\"]\n",
    "\n",
    "listTypes = [\"vtd\", \"muni\"]\n",
    "for j, listType in enumerate(listTypes):\n",
    "    rNos, HDwts, HDTLs = list(), list(), list()\n",
    "    for filename in filenames:\n",
    "        fileDF = pd.read_csv(\"state_HD_output/legislCountyMuni/\"+filename+listType+\".csv\")\n",
    "        new_rNos = fileDF['regionNo'].to_list()\n",
    "        new_HDwts = fileDF['HDwt'].to_list()\n",
    "        HDTLstring = fileDF['HDtractList'].to_list()\n",
    "        #newHDTLs =  [ast.literal_eval(HDTLstring[i])  for i in range(len(HDTLstring)) ]\n",
    "        rNos = rNos + new_rNos\n",
    "        HDwts = HDwts + new_HDwts\n",
    "        HDTLs = HDTLs + HDTLstring  #(newHDTLs)  #can keep as string since we are spitting right back out\n",
    "        print(\"We read in the tractLists and weights for\",filename+listType)\n",
    "    print(\"The sum of HDwts for\",listType,\"is\", r3(np.sum(HDwts)) )\n",
    "    df = pd.DataFrame( {\"regionNo\":rNos,\"HDwt\":HDwts,\"HDtractList\":HDTLs } ) \n",
    "    outname = \"legislCounty_\"+listType+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac9fcaa4-aa7b-45c0-8550-9abe73860ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#testing - Hamilton and Franklin muni-snap\n",
    "for i, L in enumerate(HDTLs):\n",
    "    if i%47 == 0 and rNos[i] == 2:\n",
    "        fred = ast.literal_eval(L)\n",
    "        for v in fred:\n",
    "            plotPoly(tractGeom[v])\n",
    "            plotPoly(tractCP[v].buffer(0.002))\n",
    "        plotPoly(countyGeom[24])\n",
    "        plotCenter(i,countyGeom[24])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f08e5190-48d7-43e3-8b2a-5b16fdbc4222",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c0fe31e-69df-43ea-8c90-259a0a31de50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "74826290-1f52-49f5-b410-00cf30175c43",
   "metadata": {},
   "source": [
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *\n",
    "*                           *"
   ]
  },
  {
   "cell_type": "raw",
   "id": "5958a892-f3ed-4467-af71-f1905a467583",
   "metadata": {},
   "source": [
    "ALL BELOW ARE FROM ILLEGAL OPTION B OR A; IGNORE.  Further below, unused from prev legisl codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "edae75b6-ee53-4526-80d3-7b82b41e100e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The second option is Cuy-Summ, Trum-Asht, Asht-Gea, Gea-Por-Stark, which necessitates a vertical slice of Por\n",
      "These are county pairs 17-76, 77-3, 3-27, 27-66-75 (with 27 cut along SW-NE diag or E-W)\n",
      "To start option B - ID Cuyahoga's extreme Summit straddlers\n"
     ]
    },
    {
     "data": {
      "image/png": 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OseOnEwAMvKE9sVe3a9pKNRInvfXSV2GQgcItjaKqaqsZKVVYWIi3tzcFBQV4eXk1dXWEEKJZsJgtbPg0kQNb0wAYdFMH+o5uuV8Y17y9h+O7s7ny9i70uCL8/AeIJlff67csaCmEEK2Y2Wxh3fv7OLozC0WBK2/vQvehLftCf65Zs8KxSVAjhBCtlMVsYd37+22LU465t4fDLn3QEFUxjWppNR0VrYYENUII0QpZzBbWfbifozsz0WgVxt4fQ7uYgKau1qUhDTUtlsxnE0KIVsZitvDLh/s58pc1oLm6NQU0VHc/tZ4Rpa2HtNQIIUQrUmE0s+79fRzfnW0NaO7rQVTP1hPQALaWmlY0T6bVkKBGCCFaibIiIz8u3kPG8UK0Og2j7+1OVK+WP4bmdLbeJ4lpWhwJaoQQohXIzyzlh7d2U5BVht5NxzXTexLW0aepq9U0bN1PEtW0NBLUCCFEC5d+vIAf/7uH8uIKPP1cGP9gL3xD3Ju6Wk1GZnS3XBLUCCFEC3Z8dxZr39uHqcJCYFtPxs3oibu3vqmr1aRkoHDLJUGNEEK0UHs3pLJ55SFUFdp292fM1O44u8jHvgwUbrnk3S2EEC2M2WTh96+PsPe3VAC6DQnlytu7oNFKFg+oTrqnkQUtWxwJaoQQogUpzC7j5/f2kZlUCFhX2u53TTtZGqAGi7kyqJEgr8WRoEYIIVqI43uy+XXZfgylJvRuOkbd3Y12rS0HTT1YqlpqtBLotTQS1AghhIMzmy388e0xdq1LBiConRdjpnbHy9+1iWvWTFUNpZExNS2OBDVCCOHAivMMrH3/b9KOFADQa0QEg27qgFYnXStn4+SiBcBYbm7imgh7k6BGCCEcVM7JYr5/M4GSAiPOLlpG3NmVDn2DmrpazZ6zq/XSZywzNXFNhL1JUCOEEA4o/XgB37+RgLHcjG+oO+Omx+Ad6NbU1XIITs7WlpoKo7TUtDQS1AghhAP6YdFuW/fJTXP64uLu1MQ1chxVA4RVSxNXRNiddLoKIYSDSTmQi6HE2nXSvk+gBDQNVBXUVM2CEi2HtNQIIYQDUVWV9csPAOAX5s6IO7s2cY0cj1KZdC/xj3SS/86xZRiulcpHUaybbc8pZ9yn5rbq5xTrf0rNXZVahykaBUVR0GgVFE3lT8DNy5nyUhPZqUVotJp6B6x6Vy3uvi7o3XSV9VZAVTGWmzGWmzAZLShKZcJBRUGjqTxHDWgUBTRKrXopijX469w/hOB2XvWqQ3MgQY0QQjgQRVFo3zuQPb+lkpdWQuqBXBkc3EB+odbFPE0GM0WG5j2upjCrrElfP+N4If94rF+T1qEhJKgRQggHc/mEThTmlJO0J5sdP52QoKaB2vcO5I7nB1Je2YWHCmpV8pqqHzVy2ag1toNqfa7m7qpa8+kaz6mVZVc/V7XelKqCalaxWFQsFovtfkm+AWdXJ/zD3bGYVcwV5x/4owKGkgqK88oxlpltdUJRcHbR4uyiQ+essb6mRa3xs+59VBWLxRpMJf6RTkUzD/pOJ0GNEEI4mNIiI7lpJYD1Ai0azjvQDW/51Z3VycQ8Ev9It62T5SgkqBFCCAeSlVzEmrf3UJxnwNXLmZ7D2zR1lUQLpFROI3K0pMsS1AghhIM4/FcG6z86gKnCgk+wG+Om97QlkhPCvhxzXSz51yCEEM2calH584fj/LUmCYC23f0YfU939G4ylVs0jqrJWtL9JIQQwm4sZgs/v7ePY7uyAOg1KoLBN3W0Ts0VorFURjWOFdJIUCOEEM1WzaUQAEbc2ZWug0ObuFaiNbCl1XGwQTUS1AghRDNjsajs/jWF7d8ctWW9vXxCJwloxCVTlSzQ0ZaSkKBGCCGakZSDuax+I8HW7t+hbyBDb+mMu4++SeslWpmqMTUO1gElQY0QQjQDqqoS/2MS8T8cr7V9zNQetVLsC3EpVHc/NWk1GkwWtBRCiGZg968pdQKa/uOjJKARTaK6+8mxohppqRFCiCZkMVv4a00S8T8mAeAf7sGNc/pSUW7Cw9elaSsnWi9b95NjkaBGCCGaiMWi8vPSfRxLsE7Xjh4YwtCJnXF20aGXpHqiCdlaahwsqpF/NUII0QQqDGY2fHaQYwlZaHUaRtwZTef+IU1dLSEAx53SfVFjauLi4lAUhVmzZtm2rVq1ijFjxhAQEICiKCQkJJy3nGXLlqEoSp1beXm5bZ+5c+fWeT4kRD4AhBCOJyu5iC9ejOfQHxmgwKgp3SSgEc1LVfeTY8U0F95SEx8fz5IlS+jZs2et7SUlJQwZMoQJEyYwderUepfn5eVFYmJirW0uLrX7k7t3784vv/xie6zVai+g5kII0TRUi0rCLyls/+4oFrOKu7czo6Z0o020X1NXTYhaFKq6nxwrqrmgoKa4uJhJkyaxdOlS5s+fX+u5yZMnA5CUlNSgMuvT8qLT6aR1RgjhkEryDfyybD+pB/MAaN87kOF3ROPiIes3ieanapVuRxspfEHdTzNmzGDcuHGMGjXKbhUpLi4mMjKSNm3acO2117Jr1646+xw+fJiwsDCioqKYOHEix44dO2eZBoOBwsLCWjchhLjU0o8VsOL5P0k9mIfOScOwSV24+v4eEtCIZqvVDBResWIFO3fuJD4+3m6ViI6OZtmyZcTExFBYWMgbb7zBkCFD2L17N506dQJgwIABLF++nM6dO5ORkcH8+fMZPHgw+/btw9/f/4zlxsXFMW/ePLvVUwghaqowmjkcn4FqUfENcac4v5yU/blkHC+k+9Bweo2MIO1oAd+/lUBFuZmACA9G39Md3xD3pq66EPXSorufUlJSmDlzJmvXrq0z3uViDBw4kIEDB9oeDxkyhL59+/LWW2/x5ptvAjB27Fjb8zExMQwaNIgOHTrw0UcfMXv27DOW+8QTT9R6rrCwkIiICLvVWwjRehXllvPj4j3kpBaf8fkDW9Nw99GzfvkBKgxmwrv4MG56L5z0MhZQNH+O2v3UoKBmx44dZGZmEhsba9tmNpvZtGkTixYtwmAw2GXwrkaj4bLLLuPw4cNn3cfd3Z2YmJhz7qPX69HrZb0UIYT95GeWcjg+g93rUzCUmHD1dMLV05ncUyX4t/HAy9+F47uzyT1VzM9L/wagTbQv10zviZOzBDTCMVR3PzlWVNOgoGbkyJHs3bu31rYpU6YQHR3NY489ZrfZSKqqkpCQQExMzFn3MRgMHDhwgKFDh9rlNYUQ4mxKCgwc+SuTQ/EZZCZVj81z83Lmpkf64hXgiqnCgpOzltSDuRzfnY2qgqJRiL06kn7XtEOrk1VphANyrJimYUGNp6cnPXr0qLXN3d0df39/2/bc3FySk5M5deoUgG2adkhIiG3m0p133kl4eDhxcXEAzJs3j4EDB9KpUycKCwt58803SUhI4L///a/tdebMmcP48eNp27YtmZmZzJ8/n8LCQu66664LPHUhhDg7Q5mJY7uyOByfTurBPNuASUWBiK5+dOofTPtegThXZv6taoXxCXZD56zBw9eFUXd3IzjKq6lOQYgLpmhayUDh81m9ejVTpkyxPZ44cSIAzz77LHPnzgUgOTkZjab6W0t+fj733Xcf6enpeHt706dPHzZt2kT//v1t+6SmpnLbbbeRnZ1NYGAgAwcOZPv27URGRtr7FIQQrZRqUUk9mMf+309xfHc2ZpPF9lxwlBed+wfTMTYYNy/ns5bh4evClFcux8lZa7swCOGoHK37SVEdrcYXobCwEG9vbwoKCvDykm9PQgir4rxyDm5LY//vaRTlVGcy9w1xo3P/EDpdFox3oGsT1lCIS6s4z8BHT/yORqPwwOLhTV2del+/Ze0nIUSrdepwHrvWJnPi7xxbM7uzq44u/YPpOiSMgAgP24BJIVqTqtlPjtbuIUGNEKJVUVWVlAO5/LUmibQjBbbtYZ186DYklA59g9DJLCUhABlTI4QQzZKqqiTtzeGvNUm2GUwanULXwWH0GtFGEuIJUUPNFkpVVR2mxVKCGiFEi2axqBzblcVf/0uyJcrTOWnoPjSc3le1xcNXclkJcbpaMYyKbdXu5k6CGiFEi2SxqBz5K4O/1iSRl14KgJNeS8ywNvQaGXHOGUxCtHa1WmpwmJhGghohRMuiqirHE7L54/tj5J4qAUDvpqPn8Db0HBGBi7ssIinEedWIYqyDhR0jrJGgRgjRYiTvz2H7t8fISi4CrMFM71ER9BweYUuSJ4Q4v1rdTxbAQcbOy79yIYTDK8wpY8sXhzm+OxuwdjP1GhlB71ER6N2kZUaIBqvV/eQ4U6AkqBFCOCxzhYVdvySzY00SpgoLGo1CzPA2xI6NxNVDxswIccFqzOVWHKTrCSSoEUI4qJQDuWxacYj8DOsg4LBOPlxxW2f8wzyauGZCOL5a+WkcJ6aRoEYI4VhKC41s/uIQR/7KBMDVy5khN3ekc/9gh8mlIYRDcaB/VhLUCCEcgqqqHNyWxu9fHcFQakJRIGZYG/pf1x69DAIWotE4UEwjQY0QovkryCpjw6cHST2YB0BAhAcjJnclsK1nE9dMiBaqVveT44Q1EtQIIZotVVXZ/WsKf3x3DFOFBa2Thv7XRtF7VAQaraapqydEi+VIM55qkqBGCNEsGcpM/Lpsv22adngXH4ZNisYnyK2JayZEK1AjpnGcdhoJaoQQzVDOyWL+9+5eCjLL0OgUhk7oRPcrwmUgsBCXiMx+EkIIOziyI5NfP9qPyWjBw1fP1ffHENzOq6mrJUSr5UhfJiSoEUI0G7vXp7Dli8MAtIn2ZfS93SWJnhCi3iSoEUI0OVVV+eO7Y+z46QRgnap9+S2d0Ggc5xuiEC2JWtX/5GD/BCWoEUI0KYvZwsbPEtn/exoAA65vT+zVkQ7V5C2EaB4kqBFCNBmT0cza9/dxfHc2igLDJkXT7fKwpq6WEMIxG2okqBFCNI3SQiP/e2cv6ccK0Oo0jL63O+17BzZ1tYQQ1Jj95GAtphLUCCEuuazkIta8vYfiPAN6Nx3XPBBDWCffpq6WEOI0jhXSSFAjhLjEDv+VwfqPDmCqsOAT7Ma46T3xCZaEekI0L47Z/yRBjRDiklBVlR0/neCP744B0La7P6Pv7S6LUQrRDFV3PzVpNRpMPk2EEI3ObLawqcYMp14jIxh8c0eZsi2EsCsJaoQQjcpYZuKnpX+Tsj8XRYHLb+lMz+FtmrpaQohzqMpTozhYU40ENUKIRlOcV84Pi/aQc7IYnbOG0ff2IKpnQFNXSwhRX44V00hQY0+qqrJ3Qyquns507BuEIk3rohXLSinix0W7KSkw4ublzLgZPQmKlDWchHAIjjlOWIIae8pOLWbzSuu6NbvaJjPk5o6Ed5FpqqL1Ob47i3Uf7KfCYMY31J1r/9kTL3/Xpq6WEKKhHCyqkaDGjkryDLb7WclFfPufXYR29Kb3qLa06xkggyJFi1dSYGDLl4c58lcmAOFdfBh7fwx6N6cmrpkQoiFss58cjAQ1dlRaZAQgpL03AW082L/lFGlHCkg7shevQFd6jYggelAIzi7yaxctz4Gtafz+1WEMpSYUBXqOjGDQDR3Q6jRNXTUhRINV9T851pdxubraUVllUOMT7MqVt3chdmw79m5MZd+mkxRmlbF55SH+/P4Y3YeGETOsDR6+Lk1cYyEuXoXBzO9fHWbf5lMABLb1ZNikLjJ+RogWwLFCGglq7KqsqAIAV09nADx89Qy6oQP9xrbj4LY0dv+aQkFWGTt/TiZhXQod+wXRa2SEfPgLh3ViXw4bP02kKLccgMge/lzzQAwarbTOCOHIJPmesLXUuHo419rupNcSM6wNPa4IJ2lvNgm/pHDqcD6H/szg0J8ZhHXyofeoCNrFBMiMKeEQSguNbPnyMIfjMwDw9HPhytu7ENnDv4lrJoSwC5n9JAxlJgD07mf+tSoahahegUT1CiTzRCG7f03hyF+ZnDqcz6nD+XgHutJrZATRg0Jx0msvZdWFqBdVVUncns6Wrw5jKKkcOzMigv7jo2SsmBAtkYONqbmoNuK4uDgURWHWrFm2batWrWLMmDEEBASgKAoJCQnnLWfZsmUoilLnVl5eXmu/xYsXExUVhYuLC7GxsWzevPliqm93xtLKoKYea9kERXpx1f91Z/ILg+g7pi16Nx0FWWVsWnGIj574nW3fHKW4xmwqIZpaQVYpq99I4NePDmAoMeHfxoObH+vH5RM6SUAjRAujOuj0pwv+JIqPj2fJkiX07Nmz1vaSkhKGDBnChAkTmDp1ar3L8/LyIjExsdY2F5fqgbQrV65k1qxZLF68mCFDhvDuu+8yduxY9u/fT9u2bS/0NOyqqqXGuQEL9Hn4ujDoxo7Ejm3HwW3p7F6fQmFWGTt/PkHCumQ6XhZE75FtCWzr2VjVFuKczGYLu39J4c8fjmOusKB10tD/2ih6jYpAK2NnhGjRHKyh5sKCmuLiYiZNmsTSpUuZP39+recmT54MQFJSUoPKVBSFkJCQsz6/YMEC7rnnHu69914AFi5cyM8//8zbb79NXFxcw06gkRgvIKip4uyio+fwNvS4MpykPdkk/JJM2pECDv2RwaE/Mgjv7EPXIWEEt/PCO9BVxt40IYtFpazQSEFWGfmZpRRmlWEyWRpcjkZR8G/jQWgHbzz9XVCa4adH5olC1n98kJzUYgDaRPsybFIXvAPdmrhmQghR1wUFNTNmzGDcuHGMGjWqTlBzoYqLi4mMjMRsNtO7d2+ef/55+vTpA4DRaGTHjh08/vjjtY4ZPXo0W7duPWuZBoMBg6G6C6ewsNAudT3r65XVv/vpbDQahfa9A2nfO5CMpMpxNzsyOXkon5OH8gHQ6bX4h7kTEOFJQBsPAiI88A/3wMlZxuHYg8WiUphVRs7JYgqzyynOL6ck30BxnoGSfAMlBUZUi32bZt29nQnt6ENIB2/COvrgH+7eZDOIjOUmTuzN4ciOTI7vzkJVrePELp/QiS4DQppl8CWEsK9WM/tpxYoV7Ny5k/j4eLtVIjo6mmXLlhETE0NhYSFvvPEGQ4YMYffu3XTq1Ins7GzMZjPBwcG1jgsODiY9Pf2s5cbFxTFv3jy71fNcLBaVinIzcGEtNWcS3M6L0fd0Z9CNHfh740lOHsojJ7UYk8FMxvFCMo7XCNIU8AlyIyDCozLQ8SQwwhM3L+ezv4BAtajkpZeSfryAjOOFZCUXkZdWgqni3C0vigIefi74BLniFeiGs0vDA0qT0ULmiUKyThRRUmDkyI5MjuywZuLV6bUERngQ1M6L4EgvAiM9rS10jRRQ2AKZnZmc+DsHc43z79w/mMsndLKlKhBCtAK22U+OFdU06OqbkpLCzJkzWbt2ba3xLhdr4MCBDBw40PZ4yJAh9O3bl7feeos333zTtv30D3RVVc/5If/EE08we/Zs2+PCwkIiIiLsVu+aKspNtvsX01JzJp5+Lgy6sQNgDZ4KMkvJTikmO7WI7JRislKLKSs0kp9RSn5GqS1FPVhz5QRFehHUzpOgttaLo4t760xZbzKayUsvJTethNxTJWSlFJFxvNDWbViTzkmDX5g73kFuePjqcffRV//00ePm5Wy3lpQKo5nM44WkHS0g7Wg+6UcLMJabK7NRF9j207vrCG3vTUgHb0I7eBMY6XVRrXPGchMn/ra2yJweyHgHutIxNoiO/YIJaONxUecnhHA8qoPO6W7Q1XfHjh1kZmYSGxtr22Y2m9m0aROLFi3CYDCg1V58F4hGo+Gyyy7j8GHr4pABAQFotdo6rTKZmZl1Wm9q0uv16PX6i65PfRgqZz5pnTRonRqv20CjUfANccc3xJ1Ol1Wfe0mBgZzUYrJTi8lOKSIrpZj8zFKK8wwU52VxLCHLtq93oCtB7bwIivQkqJ0XgRGeLWoKubnCQn5mKTmnisk9ZQ1gctNKKMwqO+N6JjpnDUGRXoS09yIo0gv/cA+8Al0v2VpdTs5awrv42hY/tVhU8tJLyEwqIutEIRknishOLcJQYiJpbw5Je3MA63shMNKTtt38aNvDn6BIr/PWucJgJmlvNkcrAxnTGQKZDrFBBLTxkG4mIYTDaVBQM3LkSPbu3Vtr25QpU4iOjuaxxx6zS0AD1haYhIQEYmJiAHB2diY2NpZ169Zx44032vZbt24d119/vV1e82IZyy98kLA9uHvrcffW07Z7dfIzY7mJrOQiMpOKyDxRSOaJQgqzyynIKqMgq8yWOE1RwDfUvbKrwxro+Id7NPs1e1RVpSTfQOaJIrJTimzBS35m2VnHvOjddfiFuuMX5kFAuDvBUd5NOn7lTDQaBf8wD/zDPOg6OBQAs8lCdkox6ccKSDtaQPrRfEoKjLZuyPgfk3Bxd6JNV198gt3w9HXBw0+Ph68L7t7OJO/P5ejOTE7srR3IeFW1yPQNIiBCAhkhRCXHXPqpYUGNp6cnPXr0qLXN3d0df39/2/bc3FySk5M5dcq6DkzVNO2QkBDb7KY777yT8PBw26ylefPmMXDgQDp16kRhYSFvvvkmCQkJ/Pe//7W9zuzZs5k8eTL9+vVj0KBBLFmyhOTkZKZNm3aBp25fRjsMErY3Zxcd4Z19Ce/sa9tWXlxhC3AyKoOd0gKjrUXj4NY0ADQ6hYBwj+quq0gvfEPdm2yl8ZICA1knisg5VUxhTrltIG/V0hSnc3bR4hfmgV+Ye2UQY725eTk75IVbq9MQHOVFcJQXvUZGoKoqRTnlpCbmkbwvh5QDeZSXVNTqejwbr0BXOvYNomOsBDJCiJbF7lfg1atXM2XKFNvjiRMnAvDss88yd+5cAJKTk9Foqr8Z5+fnc99995Geno63tzd9+vRh06ZN9O/f37bPrbfeSk5ODs899xxpaWn06NGDNWvWEBkZae9TuCCGsspBwhcwYPRScvFwom13/1otOiX5BjKSCiuDHWugYygxVd4vgk3W/WyDV2sEOo0xeNVUYSb9aAGnjhSQlWztgikpMJ5xX0Wj4BfqTmBbD/zCPPCvDF7cffQt+mKtKApeAa50C3Cl25AwzGYLGccKOXUkn+LccopyDRTnlZN7qgSwjq3q3D9EAhkhRL1Uz35yrM8KRXXUtIEXoLCwEG9vbwoKCvDysu8ikkd2ZPLz0r8J7ejNTXNiz39AM6aqKoXZ5dYgJ6ky0EkuwmQw19lX76azjs2J9LIFOw0NKCxmC5nJRaQezCP1YB7pRwswn5b3RVHAJ8SdgDYeeAe54h3gik+IGwHhHuhkKvtZFeWWYyg14R/uLoGMEKLeslOLWDk/HjcvZ6a8cnlTV6fe1+/m01fi4KrGcGi0jn/hUBQF70BXvANd6dTPOhi5avBq1okiMpNqDF4tNZFyII+UA3m24928nGsNRA6K9Ky1yKehzETm8ULbNOq0I/kYy2sHTG7ezoR39iW4nXXGVkAbD0nFfwE8/Vzw9GvqWgghHJaDXdLkKmEnFltQ03wGnNpTzcGr0YOqB6/mnCy2dVllJhWRm1ZCaaGRpD3ZJO3Jth3v6e+CX6g7hTnl5KWX2AahVdG7Wcf/tIm2zgLyDXGTlgUhhGgijtqHI0GNnVjMlUFNK1q+QKvT2LqdIByw5lzJTi6qDnROFJGfUUpRTjlFOdULlHoFuBAc5U1wlBehHbwJiPBsVb87IYRo1hwzTY0ENfZS1f3U2tdkcnLWEtrRh9COPrZthtIKMpOLKMoux9XLmeB2XpLpWAghmrGq4baOdk2ToMZOLC1oTI296d2ciIiWgR1CCOEo1Mq5Go42DKBlDgBpAq2x+0kIIUTLVN1S08QVaSAHq27zZet+kphGCCGEg6u+pjnWRU2CGjtx1P5HIYQQ4nRVs58c7ZomQY2d2N4ADhbVCiGEEKdz1N4HCWrsxNZS42BvACGEEOJ0jtr7IEGNvVQlKnKwN4AQQghxuurZT01bj4aSoMZObFFtE9dDCCGEuFjSUtPKOWpUK4QQQpzOUceJSlBjN5XvAAeLaoUQQojTyUDhVs5Ro1ohhBDidBYHXfpHgho7cdSoVgghhDhd1ZgaR8uSL0GNnUhLjRBCiJbCNk5UgprWSfLUCCGEaCksFmtUI2s/tVLSUiOEEKKlkFW6W7uqqEZ+o0IIIRxc1ThRjVaCmlbJFtU2bTWEEEKIiyazn1o5i4NmXxRCCCFOVz2j17GuaRLU2IlqdsymOiGEEOJ0tu4nB/uiLkGNnVjMjvkGEEIIIU5nsU3pbtp6NJSDVbf5sjjooCohhBDidNJS08pZzNawVqOVX6kQQgjHJqt0t3KOOlJcCCGEOJ2jXtMkqLETGVMjhBCipVAlqGndZPaTEEKIlkLG1LRyZglqhBBCtBAWWdCylXPQQVVCCCHE6WwtNQ52SZOgxk6qln4SQgghHJ2MqWnlZJVuIYQQLYVM6W7tbG+AJq6HEEIIcZFkTE0rV9X75Fh/fiGEEKKu6tlPTVyRBnKw6jZfVW8ApPtJCCGEg2uVq3THxcWhKAqzZs2ybVu1ahVjxowhICAARVFISEhoUJkrVqxAURRuuOGGWtvnzp2Loii1biEhIRdTfbuyjamRMFEIIYSDq76mtZKgJj4+niVLltCzZ89a20tKShgyZAgvvfRSg8s8ceIEc+bMYejQoWd8vnv37qSlpdlue/fuvaC6N47KqFY6oIQQQji46paaJq5IA+ku5KDi4mImTZrE0qVLmT9/fq3nJk+eDEBSUlKDyjSbzUyaNIl58+axefNm8vPz61ZWp2tQ64zBYMBgMNgeFxYWNqhODaFWDqqSmEYIIYSjs7Sm2U8zZsxg3LhxjBo1ym4Vee655wgMDOSee+456z6HDx8mLCyMqKgoJk6cyLFjx85ZZlxcHN7e3rZbRESE3ep7Otv0N0cLa4UQQojTOGqemga31KxYsYKdO3cSHx9vt0r8/vvvvP/+++ccfzNgwACWL19O586dycjIYP78+QwePJh9+/bh7+9/xmOeeOIJZs+ebXtcWFjYqIENOF5TnRBCCHE61UHnvjQoqElJSWHmzJmsXbsWFxcXu1SgqKiIO+64g6VLlxIQEHDW/caOHWu7HxMTw6BBg+jQoQMfffRRrcClJr1ej16vt0s9z6eq+0laaoQQQji6VtFSs2PHDjIzM4mNjbVtM5vNbNq0iUWLFmEwGNBqtQ2qwNGjR0lKSmL8+PG2bZbKrD86nY7ExEQ6dOhQ5zh3d3diYmI4fPhwg16v8VSFtU1bCyGEEOJiVQU1mUlF7Nt80rpNtf6vqhVHrbpfY5mgNtG++Id7XNrK1tCgoGbkyJF1ZhxNmTKF6OhoHnvssQYHNADR0dF1ynz66acpKirijTfeOGt3kcFg4MCBA2edKXWpOWpTnRBCCHE6TWULzdGdmRzdmVnv47wDXbnj+UGNVa3zalBQ4+npSY8ePWptc3d3x9/f37Y9NzeX5ORkTp06BUBiYiIAISEhtplLd955J+Hh4cTFxeHi4lKnTB8fH4Ba2+fMmcP48eNp27YtmZmZzJ8/n8LCQu66666GnEKjcdRERUIIIcTpeo6IoMJoxmxSbV/WFUUBperLu2L9ad1EhcFM0t4cyoormq7SXOCU7nNZvXo1U6ZMsT2eOHEiAM8++yxz584FIDk5GU0Dcy+npqZy2223kZ2dTWBgIAMHDmT79u1ERkbare52ITGNEEIIBxfY1pOr74up9/4FWaUk7c2pzq7fRC46qNmwYUOtx3fffTd33313g4453bJly+psW7FiRcMqdonJKt1CCCFaq6prX1MHNZLU306q89Q0cUWEEEKIS6xqllRV0r6mIkGNnUhLjRBCiNaqamCxapagpmVQZUq3EEKI1qmqpUZVq3sumoIENXYiU7qFEEK0VpoaSfqasgdKgho7ke4nIYQQrZVSI5poysHCEtTYi3Q/CSGEaKVqLqcgQU0LIC01QgghWquaQY1FghrHZzFb16vSaCWoEUII0bo0lyufBDV2YqmcxiZBjRBCiNamZi+FDBRuAaqa2ySoEUII0erUvPTJlG7HV9VSU7NfUQghhGgValz6pKWmBaga7a3Vyq9UCCFE69Jcvs7LFdhOzNJSI4QQorWqOfNXWmocnyoDhYUQQrRStYfUyJgahycDhYUQQrRazWRMja7pXrqFaeLl1oUQQgh72bf5JHs3pNoubeYKC4Yyk7UVRrWOI1WpvPRZ1GZzCZSgxk4UjYJqVps0PbQQQghhD7vXp5KXVtLg47wCXdG7N11oIUGNnSgaBcxqk6aHFkIIIeyh6gv6ZePaEdbZF0UBF3cnFI2ColQm26v8qSjVk2TcvJybdBawBDV2UvUHVS1NXBEhhBDCTtpE+xLWybepq1FvMlDYTqpmckv3kxBCCEdXPYPJsSa/SFBjJ1UtNdL9JIQQwuE5ZkwjQY0QQjRE8e+/k//tt5gLC5u6KkI0GgeNaWRMjb2YK6yDaXROEicK0RKpFgv5X35F+rPPApDu7IzHiBH43XUnbn36NHHthLCzqu4nxbHCGglq7EBVVUwma1CjlaBGiBZHVVUyXnqJvOUfV28zGin66SeK1q4lYNo0XGJ6oOicUHRa0GpRdDqc27VD5+fXhDUX4uI4WEwjQY09WMyqra1Oq5OgRoiWJvPV12oFNEGPzMF90CAyFy6kZNNmshcvPuNxWj8/Ovz8E1pPz0tVVSHsorkk02souQLbQVXXE0j3kxAtUe4HH9R6XLD6e5zbtyd8wX/wGjcO1969cYmJQd+tK/rOnXHu0AGNmxvm3Fzyv/yqiWotxEVw0EE10lJjB2ZTdVAjLTVCtDyBs2aStfAN22NDYiLHb7iRditXEP76a2c8Jv+rr0h7+t/kLl+O3+Q7UJycLlV1hWi15ApsB6bKlhqNTrFN7RZCtBwB06YR9uorBEx/APehQwEwJiWRcv80LAbDGY/xGj8ebUAApvR0Cn/6+VJWV4iLplY21SgO1lQjQY0dVLXUNGVqaCFE4/IeP57ABx+k7dIlRK3+Do2XF2UJCZx65FFUs7nO/hq9Hr9JtwOQ88EHqBZJNy4ciIN2P8lV2A6UyuHhDjquSgjRQC6dOxP2yssAFK1dS96nn9XIwFrNZ+JEFDc3DAcOkLNkyaWuphAXzdFmP0lQYwcabWVQY5awRojWwnPYMLzGjwcg48UXyZj/Qp19dL6+hDz9NABZb75FyfY/LmkdhbhQMvupFdPIEglCtErBTzyOW79+AOR9+inm4pI6+/jcdCPeN9wAFgupM2ZQunPXJa6lEBdOcbCmGglq7KB6hW71jE3QQoiWSefnR9hrr9oeH7vmGioyMuvsF/LsM7hddhmWkhKSp0whf9U3l7KaQjScg17LJKixA6XGb9FB3wdCiAvkFBJC0GOPAWDKzMRw6FCdfTSurkS8+w4ew4ahGgykPfkkaf/+N5by8ktdXSHqxVEvZRLU2IHFVDn1TaPYuqKEEK1HxalTtvumzIwzznTSuLnRZvF/CZz5ICgK+V9+RdLtt2NMSbmUVRWiflrj7Ke4uDgURWHWrFm2batWrWLMmDEEBASgKAoJCQkNKnPFihUoisINN9xQ57nFixcTFRWFi4sLsbGxbN68+WKqbzdVeWq0Ogf76wshLpopJ4e8j6uXUEh76mlOzpx1xn0VjYaABx6g7fvvofX1xbD/AMdvupmi9esvUW2FaBgHG1Jz4UFNfHw8S5YsoWfPnrW2l5SUMGTIEF566aUGl3nixAnmzJnD0MrkVjWtXLmSWbNm8dRTT7Fr1y6GDh3K2LFjSU5OvtBTsBuzLGYpRKul9fXFOTKy1raidesw5eae9Rj3wYOJ+mYVrr17YykqInX6DDJffx3VZGrs6gpRL9XdT44V1VzQVbi4uJhJkyaxdOlSfH19az03efJknnnmGUaNGtWgMs1mM5MmTWLevHm0b9++zvMLFizgnnvu4d5776Vr164sXLiQiIgI3n777Qs5Bbsy21pqJKgRorVRNBr877uv1jZdUBAad/dzHucUEkLk8o/wvXMyADlL3yN5yv9hyspqtLoKUW+VA0RbRUvNjBkzGDduXIMDl3N57rnnCAwM5J577qnznNFoZMeOHYwePbrW9tGjR7N169azlmkwGCgsLKx1awxVLTWymKUQrZPiZF1GT9HrCY2LI2rV12j0+vMf5+xMyJNPEr7wP2jc3CiNj+fYTTdRGh/f2FUWokVq8FV4xYoV7Ny5k7i4OLtV4vfff+f9999n6dKlZ3w+Ozsbs9lMcHBwre3BwcGkp6eftdy4uDi8vb1tt4iICLvVuSZpqRGiddMFhwCguLjgNfZqdAEBDTre6+qraffVV+g7dcSclc2Ju6eQ8/77kiJCiAZq0FU4JSWFmTNn8sknn+Di4mKXChQVFXHHHXewdOlSAs7zQXB6EiBVVc+ZGOiJJ56goKDAdktppFkGMqZGiNbNrV8sTuHhWAoKKPzppwsqQ98+inYrV+J13Xgwm8l89TVS//UvzI3UwizEuaitYfbTjh07yMzMJDY2Fp1Oh06nY+PGjbz55pvodDrMZ1jU7XyOHj1KUlIS48ePt5W5fPlyVq9ejU6n4+jRowQEBKDVauu0ymRmZtZpvalJr9fj5eVV69YYLJXLI8h0biFaJ0Wrxfv66wEo+O67Cy5H4+ZG2MsvEzJ3LoqTE8W//Mrxf0yg/MABe1VViPqpDGpa9CrdI0eOZO/evSQkJNhu/fr1Y9KkSSQkJKDVahtcgejo6DplXnfddQwfPpyEhAQiIiJwdnYmNjaWdevW1Tp23bp1DB48uMGvKYQQ9uZ5lXWMYfn+AxfVbaQoCr4TbyXys89wCgujIjmZpIm3kf/11/aqqhDnpVZHNQ5F15CdPT096dGjR61t7u7u+Pv727bn5uaSnJzMqcpkVImJiQCEhIQQEmLtd77zzjsJDw8nLi4OFxeXOmX6+PgA1No+e/ZsJk+eTL9+/Rg0aBBLliwhOTmZadOmNeQUhBCiUTh36AA6HZaCAkwZGThVft5dKNeYHkSt+ppTjz1O8caNpD31NKU7dhLyzL/R2Kn7X4iWxu6DQFavXk2fPn0YN24cABMnTqRPnz688847tn2Sk5NJS0trULm33norCxcu5LnnnqN3795s2rSJNWvWEHlafgghhGgKGmdn9FFRABgqv8xdLK2PD23eXkzgrFmg0VCwahVJE2/DeOKEXcoX4qyqGmocrKXmooOaDRs2sHDhQtvju+++G1VV69zmzp1b65hly5adtcxly5bx7bff1tk+ffp0kpKSMBgM7NixgyuuuOJiqy+EEHaj79wZgPKDiWfMuD537lyio6Nxd3fH19eXUaNG8ccff9Qq4/7776dDhw64uroSGBjIDTfeSPawK61ZiP38MBw8yA9jxzIiNhYfHx/8/f257777KC4uvpSnKkSzJNN1hBDCTpzbW1tq/ty27YwZ1zt37syiRYvYu3cvW7ZsoV27dowePZqsGgn3YmNj+fDDDzlw4AA///wzqqoyevRoXPr3J+qbVRRGRzMlMZGgo8f4cepU/vfDD+zbt4+77777Up6qaOFUW0uNYzXVSFAjhBB2YDh8mILvVlNisfCvH74/Y8b122+/nVGjRtG+fXu6d+/OggULKCwsZM+ePbZ97rvvPq644gratWtH3759mT9/PikpKSQlJeEUHEzCmNE4u7nx7+Bg/L7/gaD//peFzz/P119/zZEjRy71aQvRrEhQI4QQdpAR9xIVycnMz0hn3HXXnTfjutFoZMmSJXh7e9OrV68z7lNSUsKHH35IVFSULXmo0WTCxceHiDffROPuTtlfO8iZ8wgAW7Zsse9JCeFgJKgRQgg7cOnVkzWFhew3GpkT3ZUTd0zGeOwYqtFYa78ffvgBDw8PXFxc+M9//sO6devqJB5dvHgxHh4eeHh48NNPP7Fu3TqcnZ0BGDFiBOnp6by7Zzdhn31Kebt2LDh8CICjP/xwaU5WtHhVvU6OltVagho7sFgqF/6S5HtCtFqlY8YQl53Fy8EhlHzwAaV//YUpO5uyv/+utV9VDq6tW7dy9dVXc8stt5CZmVlrn0mTJrFr1y42btxIp06duOWWWygvLwege/fufPTRR7z++uv4du/OwN/W06FrN/y1Wso2biLv888v2TmLFswW1DRtNRpKgho7sC2TIGs/CdFqJRw8SE5FBRNOJBGTeJCYxIPEl5Xx3pYttTKuu7u707FjRwYOHMj777+PTqfj/fffr1WWt7c3nTp14oorruCrr77i4MGDfPPNN7bnb7/9dtLT0zl58iQ5OTm88uMP5FkshDs7kf7c85Tt3XtJz120PLYBwhLUtD4WWftJiFZv5MiR7Nmzh42vvMqqdlGsahdFn46dmDRpEjvj46G09IzHqaqKwWA4Z9ln2yc4OBjzho28dVl/9MBgN3cUvR5F16C8qkLU4ajdT/LOtwOzyfpH12ql+0mI1srT05OYmBjUHj1IOXyIkk2b0Z1MxeXwYfT33U9CYSGfxvTgljlzCA0NJScnh8WLF5OamsqECRMAOHbsGCtXrmT06NEEBgZy8uRJXn75ZVxdXbnmmmtsr7Vo0SIG9e+PZds2Vr/0Mq9lpDOnc2c6zpmD9403ovP3b6pfg2gpKqMaB4tpJKixB+l+EkJUURSFNm+8Qcr90+CrExgOHcISFIxWVTmwcyc333wz2dnZ+Pv7c9lll7F582a6d+8OgIuLC5s3b2bhwoXk5eURHBzMFVdcwdatWwkKCgLAYjCw5Ysv+PdDD1FiNtPe2ZmXrh7Lgz98j6KRzyBhH9JS04pVBTUaCWqEEIDG1ZWId97mK52Okvh4nNu2haNHeXf4CMJefYWidb+gmk14jR6NrsbMp7CwMNasWWN7rBqNGI4coXzvXtJXrqR8/wHKExN5tqyMZzt0ROvvj/+Uu/G76y4JaIRd2fodHCumkaDGHizmyu4nnXQ/CSGsNG5uRLz/HmpFBcXr13Ny1kOU/P47R0ZdhVo5vib7v4tpu3QJLt26oaoq5Xv3UvTreixlpZTtSqD84EGoqKhTti40FP//+z98/nEzGlfXS31qTUJVVd7ZeIyjWcVoFQWtVqGdvxv3Xt4ejcw8tT9b95NjRTUS1NiBdD8JIc5EURQUZ2fcBw3CqW1bKpKTUUtL0XfpglpejvHECY7fcive111H2a5dGI8fr1OGxssLl27daty64hwZiaLVNsEZNZ3EjCJe/ulgne0DovzpFeFz6SvUwjnY6gg2EtTYgXQ/CSHORevtTftvVlH29z60nh7oo6OxlJSQfNfdlO/fT8GqVQAoLi54jhyJLigI53btcB8yGKfwcIdbf6cxpOVb8/SEertwx8BI3t9ynNwSI2UV5iaumWhOJKixA9vsJwlqhBBnoXF3x31Af9tjracnutBQ2L/ftq3Tli1oPdybonrNXmaRNajpEuLJjOEdWbUzldwS43mOEhfMQZPvSVBjB7Y8NTKlWwjRAMakJNt9r2uuuaiAxlxcgvH4cYxJx1H0ejyvuqpFtfBkFVnz9AR56mttbzln2ExJUNP6mCX5nhDiPEpMZnYVldLNwxU/J+tHry4wEOPRowB4jhpZr3IqTp3CcPQYxuPHMBw/jvF4EsZjxzCdttRCuy+/wDUmxr4n0YSqgprAyqDGwa61DkepDBdVB/tNy1XYDmTtJyHEuRwrNXD5nwf5R8JRum/5m6v/OsTrv+8g/68dtn3cr7jinGWoqkrav//NkREjSZk6lYwX48j/fAWl27fbAhptQACK3nrRN+fnN9r5XKy4uDgURWHWrFm2baqqMnfuXMLCwnB1dWXYsGHs27fP9nx2sZH0zx7n0au7oigKv80ZzomXr+XpmVNrld2uXTvrAO0at8cff/xSnVrLUXU5c6yYRlpq7EGtDGpkWqEQ4kzeSs4gzVCBTgGTCglFpSSgJen6W5n+9Sfoo6PRuLtjLigAjQYUjfVLkkYDGg2KopC16L/kf/kVaDQ4t49CH9Ue56ioyvtROEdFofXy4thNN2HYf6CpT/ms4uPjWbJkCT179gRg+bYkViecInHtJxz86SMuu/MpYoLasuWrJfQaeAVh975DRLA/J/PLAOgx4ibWffpfbn57K8k5pTwx9XJb2eWVg4afe+45pk6tDnY8PDwu4Rm2DI7acylBjR3YBlI56JtACNE4Mg0VfJKWw+dpuQDEdW7DCF8PYrdbpyaHZmcA4HH5EFLuu5+SzZvPW2bo88/jc/NNZ9/B9s26+X0gFRcXM2nSJJYuXcr8+fMBWPjLYXKKDZxctwLPAbdwyrcXVEDAuNmkLLqDkgMbOakfaysjpxxCQkLw9A1Aayji3e1pHM43E5+Uyy8HMskoLMfN3YOQkJCmOs0WxdEGCkv3kx1IS40Q4nR7ikoZFn+QV46nA+DnpGWAtwfhri4EFuYDkOlrzSbs0r07Jdu3n7tAnY7Ahx46d0AD1VehZvhVe8aMGYwbN45Ro0bZtuWWGDEVZGAuyeP56bfx9qS+DO8SiKJzwiWiB4aTBwjwcObVf/Qkws8NQ+ImAgIC+PO1u8lb/z7bDqYS97+D/HLA2gVXYVZ55ZVX8Pf3p3fv3rzwwgsYjTJLqsGa4funPqSlxg6qgpqWNNNAiNbEoqr8mlPIrqJSBnp7oAImVcWsqphUFZOK7f7Xb/6HlXHzueqe+7hl7nzMqkpJcQnfvvQ8e9b+REleHj7hbeD6W9GO/wfR7i78s20Qo7xc+fdjj/D5559TXFyC2rc/39w/iyk/fIkxJRUs1gkHiU88Qdx/F7Fn717c3dwYOmQIX33yCYpOh8bN7fwn00y/Wq9YsYKdO3cSHx9f5zlzcR4AN13eg7CwUMbGhJKSW8q0YysozUnnt6evAiB/xr1ERUUREhLCut/jee6Zf6MpP8VNT7+D2aLyXcIpvPpdx7tzbqF9eDB//vknTzzxBMePH+e9996r9Zo/7DlFal4Zep0GvU6Ls06DXqfBWaehnb87BWUVdA/zwsVJi7byC+uGxEzmrt6HWVVx0WnRO2lw0WlxcdKi12lwcaou5x+xbejXzq9xf6kmI2z5D2i0cMUcuxZdvUxC83w/nY0ENXZg+2IkLTVCOCSzCpP3VmXzzTjrfhUH95H/4Qfo2nfi97xi9hw+CUDha89hTPgLr0efwyckDMNf2yhaGEf/NuF8/+B9eOq0PPDAA3z//fesWLECHx8frvy/+zj1/OPs7xKNz5YtYDaztqiQebNm8mJcHCNGjEBVVfbu3YvWy6vhJ9WMPo5SUlKYOXMma9euxcXF5az71fxiGOHnRpi3CymF1ZmTa46T6dGjB1f060m/fv0IN6UzbsRga1Bz2Q0MHXoF/h56evbsia+vL//4xz94+eWX8a9cvXzd/gz++dmuetdfq1Fw0iqUV1jqfcyB9CK+mzGk3vs3WFk+LIwBQ6H1cf/7wOUC3idnU5Wnxn4lXhIS1NhBdUtNE1dECHFBTl+2rbuHC1pFQVd50ypAWSm/vvQ0w+a+xL73/ou/mzNDgnzQKQorD+2j980TGHLNGOtx/Xry0drVDM85iadOS0FBAe+//z4ff/yxretl9L3T+fah+1kd04vef/6JSVWJy8zkpddf575p02x16dKlS8NOphl+s96xYweZmZnExsbatpnNZjZt2oSiWUTIPe8AkJaWRmhoqG2fzMxMgoODz1pu3759UbQ6Xlr5G2/tqc4srK3xBXPgwIEAHDlyxBbUnMyzrr1VsO0L8jctp/voicTe+hBlRjPbD50kf+MySg9tx1JehNYrCK9+1+HZ5xpbmXd2d2HjxwvY/dcfGI0G+gwexuSH5uLi5cfhzGI+/SOZ/T8uY/Cnj5CQkICzszP59p6Nln2oOqBpBLYAs/m9nc5JxtTYgUzpFsKxKYpCtLu1BeGznu359bJo1vbrwprYzqzu24lv+nTC690F3HPTDfx6/2Si3V24yt+bJd3bsbhbJBNGDqdi2yZmeWiI6xTOyNTD5Ccd49qxVwPWi3pFRQWjR4+2vebwjlHo2nXgD5P1Yry/vJwMkwmtkxN9+vQhNDSUsWPH1prWXC/qhXWHq6qKscyEscyEocxEeUkF5SUVGMtNVBjN1p8GMxVGMxUGM4XZZbbPvvMZOXIke/fuJSEhwXbr168fkyZNYtuff+EdFI7W3Zf3V35nO8ZoNLJx40YGDx5cpzyLRWXjoSz+88WvqGYTWo/a3Tw1xzfu2mVtkakZLA3uGIAh7RBFu38muF1n+kT48tH/9eeLaYMYU/QzQQUH+XHVSvb+vY9X5j5Bwa/v8u8eJax/+ErWzxzIp3Pvw99Dz+aNv/HHtq0Eumn54NkHuHNQJONiQm31v3r8DTzwwAP1/yM0hPm0hU41jdVG4VhRjbTU2EHVFyMZKCyE42rn6szBknKSygx1njvXeBCAN998k6lTp9KmTRt0Oh0ajYb33nuPyy+3TjdOT0/H2dkZX19f2zE3DIjlUR8/0lHYP2wkGbt3QPIJnnvpJRYsWEC7du14/fXXufLKKzl06BB+fvUdn9HwgcLGchPfLthFVnJRvY8B6H1VW4bc3PG8+3l6etKjR49a29zd3fH396d/397ceELL4n7X896iBYwa2JtOnTrx4osv4ubmxu233w5zvTmaa+HTvRVc0cmDdm4GfsvoyPvrDuMWHIk+vCsAhpMHMKYlcmBvCEEBfsTHx/PQQw9x3XXX0bZtW9trh7hC9vev4X/1v8jfuoI/jufYntu2bRt33XUXY66yJkPs3qUjnyx7n6TEvbS/4xbWrt1KUlISu3btwquyW/DDDz/Ez8+P9evX4x7VBwCl3y0sK4T72jasS6jIWIRFteCkcUKn0aHT6NAoGshLgj+XgtYJRs2FsrzaB2qdGvQ69dUMG/7OSVpq7KBqaXZFfptCOKwoV2vSuuOnBTVV40E++eSTs44HefPNN9m+fTurV69mx44dvP7660yfPp1ffvnlrK/X1t+HwPJSUODf10xgW+wgAJ566iluvvlmYmNj+fDDD1EUhS+//LLe56FWD/Kr1/5xcXHoXZ14+/OXzvj855sW8M93R/Lbnq9rba8wG3nhP0/j7x+Au7s748dfR2pqaq196psIb9KASLwG3Ix73+uY9sB0evfuzeeff87IkSPx9PQE4NfjJhZuNzBiaQ5RbxTz/o8J3NShgpS7cuntZ+SmPuGM6B6O67FNDBk0gPbt23Pbbbfh5OTEBx98YHutvLw8+vTpDcVZ5K+OoyInlbTcItILrGtLXX755axevZqTJ0+iqiq//fYbhw4dYsyYMQAYDAYURUGvr16uwcXFBY1Gw5YtW+gd4cPlHQNw1mqwqJBTXDdIPpuVB1cy5PMhXL7icgZ8NoA2N7dBq9Faf3d+UShXv0jIDc/BkmGwcpI1YeGGcsJeL8LVw6tOwkKA+++/nw4dOuDq6kpgYCDXX389Bw/WXe38xx9/ZMCAAbi6uhIQEMDCLyr/Tg4W1EhLjR3I7CchHF9VUHOstPb033ONB1m0aBEFBQU8+eSTfPPNN4wbNw6Anj17kpCQwGuvvcaoUaMICQnBaDSSl5dXq7XGUzVj0buS6+nNz+2trQ3/ySgi6vc/uWpIf/R6Pe3btyc5Obn+J2LLm3X+z6OqRHidO1hfu020L+Om97R+linw3XffUrQtlbDQMAbd1IH7/nUlqqpy6I90Hpg+nb9PbOOOYY/h7uLFt9vfZfSoq9m7bzdabfXg3rMlwtuwYYNtW7cwL/pG+rJLuZ1bJ93MFy8/jJeXl20MDICrDm6/+Vq81HziPtvC97e70zvE+jrfucfB2B/Ytq+MH+LSmT9/PuPHj+edd95h0aJFBAQE2MrR6XRotVrWrV3LZ9+vY+l/4ije/ROhPq7E/7WjTquboihceeWV3HDDDZSVlTF06FBcXV157LHHePHFF1FVldDQUCwWC/PmzWPevHkAdBg1iQKjltc2LcfZ2dn2+nPnzmXFihWkpKTg7OxMbGwsL7zwAgMGDGBX1i5UVE4uO0nxvmIqcipQNHBVlJanhzrTyV9rHd91ytql9n/flbF8jwmtAm56I0cSfueqwX1IfKYnnm560GiJLc1k0s2+tA2MILfEzNzv/mD0kL4cX3gNWq0OnFz5+u8ypi78gRcf+ycjlryF6uLN4n/XDmIdhbQt2IEqY2qEcHjt3axBzc7CEmYeSObBAyf414ETrAnvxMRvf+YfX6/hH1+v4eav1xDYvSddx9/A+vh4zGYzFRUVaDS1P061Wi2WymnasbGxODk5sW7dOtvzaWlpJCYe5K3bbmJeSTa9vL3ByZlThYVMKzDx5KeruHXZl+xPTMQ/MKj+J1L5mueb/VQzEZ673toa4u6jR+esReukIT0jjQdnPshnn32Kk7MTWp0GJ70WZxcdflHObE/8HxNH/oseHfoTEdCJycMfJ/HQgTqtU56enoSEhNhuZ8vuO2lAJBZjGW/++0HeeXdJreAPYHIvZ6bePZH7XvgYgMTYZzl17Sfg3RZyjsBH1/LQg//kwQcf5PHHH6d79+74+/vTvXt30tLSSEtL48svv8RkMrF06VK+SfdmfYoZjZuPrY/FYDLXanX7488/adutE7/88gv9/zGGB/77PKfyswkKCmL16tV4eHjg7e2NxWIhNDSUu+66y/Za/tH9Kdr9M4EhYbXOo3PnzixatIi9e/eyZcsW2rVrx+jRo8nKyrK9X66/8np+XPkjD0x/gLZtgtBrFSatKifQx4tAdw3E3MJXkS/w0R4z13Vz5+/p7vzxf668OlJHqaGCz37bBxl/Q9pu7uuQxhVuh2lXsou+7GH+wGJScstIiv8ZDv0P096vmfmfL3j1SjPTjG/T+Zur6PLtGAaEdQBAtZhxJNJSYwe2zxAJEYVwWJ3dXNAAeSYzK9Nzaz/pVTuoyNc5UaJzZZt3CFd4eXHllVfyyCOP4OrqSmRkJBs3bmT58uUsWLAAAG9vb+655x4efvhh/P398fPzY86cOcTExHD99deh1Wq5H7hr8//4ZNk7aAJDWBIcSsmXH2PUaFkc0omrDx2hW+fzj1+xDRTWnPsDqWYiPGP54+AGfqHWVcItFguTJ0/mkUceoXv37nWOPXjkb0xmEy8u+xe+vr5sXXWEXWshKqITW7dutXXVALz88ss8//zzREREMGHCBB555JFaLRdVru0ZytR73sWpXT+cI3vVei7RrS9dSnfC0Q3Q0TpNustlVxHWuzd06AnLriUz+RB//FXMpAk3MHjwYI4ePYqLiwvOzs627MJVrUNTpkzBXDXIWa2epm02Gmu1ur3x21cc+/sA7tHubP59NSmD9lAxsYLjDx/nf2v+R2xsLDqdjt69e5OTk0P37t0JCQmhuLiYv1e+iv/V/8Llz2UU1DiX22+/vda5LViwgPfff589e/ZQpFrHNF17+7Vc1fkqft/4O5mZ+WzXmMkqVbl+U2fefPFp2va+kn+2aYOqqjzz/s90jvQCs5EuFjMrCh9mq96d+yc/ar04WUxgqQCLiZLiEj5c9DlR4fFE3PEKaBV27trLyaLX0QR1oc/Sg6QXGukdUsKIfkdw8w60zrIiAEchQY09XOBsAyFE8xGkd+KTnu3ZV2xdY0ih+t+0UuMG8JyTjgKsaziBdSDxE088waRJk8jNzSUyMpIXXniBaTWmZv/nP/9Bp9Nxyy23UFZWxsiRI1m2bFmtrpr3li4hd9KdrH3hSVSjgbadu+D8zMvkBofxTPx+vmpAUHOu7qfTBz6bKmdghbS3Dmp9+eWX0el0PPjgg2c8/vSBz1qdNYDy9fYnPT3dtt/MmTPp27cvvr6+50yEB/Dt11/inH8Cl3+8zKfba3e3FUSMgsSduGTtrlsZ30i4+weOzRsBFDN33jxee+Uleg8eyQMPPMAff/xBcHAwbm5uuLu7ExYWxkdffMudH/yJk0aD2/YlHD96CENZKRpFrdXq9kf8dlSzis5fh5rnDKoWJ19wCXPn999/twVv5eXlFBcX88ILL/Dpp5+iKAp+Xfqjbdcb9c+z/hkwGo0sWbIEb29vevXqxcLNCwFo49kGgAEDBvDKtIH88etWfjlmoshgYfC1k/j444/JyLDmU5p8z/3k5OTQu3dvXnvtNYKjunLixAnoMML2OosXL+bRRx+lpKSE6Oho1m3chnMHa0vMscMrAJi7Lo8Fb35Ku/BgXn/lRZ779iX+fevHqCbH+rYuQY0dWGRMjRAtwgh/L0b4n3+2yoBf1zN2xyF2F5WiqiohISF8+OGH5zzGxcWFt956i7feeuus+zg5OfH9F5/X2rY/8QijUgrYEtaWLX8lcHm/3ud8HTMKxe5h5OZaKE0tro5tFFBQOJmWyoP/epCvV66mvMBC/qkCzBUqigL+bTzZsWMHb7zxBjt37qz3Z1rVbqYKC+YKC6WF1nFJ998zw/q8Bjp36Iq7qye33zGR5+e9gL+/v6385ORkZj44k7eXfcUzGwrZtj8Db3N1C0qHEXdiOvgaXUyJbDl6hinuvpFYrnkd3riR+/tqmVKxHNpP5Nlnn2XatGmMGjWK2267jfvuu4+MjAwUdz+cA9uh12kI9vfm5AktBqzjfWq2uqUfSwYNFP1ZzKI33mLM+H9w3eqrsVgq+HbjWu46ehfbtm2jqKiICRMm8PTTT7No0SI++OAD/Lt7o804hmo0oKoqCQkJAHTs2JENGzYwceJESktLCQ0NZd26dfj5+3Gy2JrMsY1nm9qBSICGLf/nQUjcGjp06MDy5cttpz5r1iz69Oljmyl3zTXX1Pm7TZo0iauuuoq0tDRee+01brnlFn7//XdcXFxsXV5VA9QBPvz8GwJ9A9l1bCO3qdfX6z3QXEhQYwdVrZcypVuI1qGbhwtOikJuhZnkciORrvrzH3Shr9WlIyP//IZ1baKYd/gkq3tE43qOrLwJQTeSFdURvi4C6jYT7D6+hazsLIaNqs52a1EtHE3fg7unCy+//DKZmZm1pkCbzWYefvhhFi5cSFJSUt2Bz5UX0azMTE7tNvDho1vOWLf8EuvPBf/8inbBXWvVKTMrkwnXDqtVp+2/b2HRokUYDAa0ivWDNnP7Z2csO7STtcuqW7sQyD8B/+nOWPcgBvbrQ3l5OaNGjeKRRx5hxowZTJgdh1f/GzGYrGWWl1tnPv12dC+Tnp3NyreW8o+Jt5CbnQMWaHdLHyb/3xTcnJzp6j6GQ4a9/L0tnq5du9KuXTuef/55HnroIVJTU/nuu+94+eWXmTNnDuzZaKtfnz7Wqd6//fYbw4cPJyEhgezsbJYuXcott9zC9+u/p8JSgU7REeIWYgtEtvz4At+8/Sk3fV3OH69oiYmJIS0tzVZubGwsffr04cMPP6RNmzbs3r2b3r171/rdeHt74+3tTadOnRg4cCC+vr5888033Hbbbbb8Pd26dbPtr9frCfIKIrc484y/6+ZMgho7kCndQrQueo2Grh4u7CkqI6GotFGDGoCZPTuxIaOMvSHhfLhmPdNvuuas+xY6W8f/6F0UNM5OoKrWHikVVFR6durP3OAPUdXqz66PfnmZXn178MIrcwkNDa01JgZgzJgxTJ48mSlTpgC1Bz7fcssttOniy6bVCZzKS+L6gfedtW4p2UcA8HKrnXOnS3hfnpxQu0tq+YZXuXJ0Px5//HG0Wi2Z+BFELgHZO85Ydrt27QgLCyMx5AbwXAtFp6Akk0NbjzN2cAyk/sWwK62zt8pP7seLGxnVNZipN75syzr8333zcI10hesh9PpgPPe7k/RKEvqhJQxacRmqqqAoKlo3LT6X+zDvyWU80L/6b1E1U+7RRx+t3KJYf/GKgqJo+OXvVIZ1tY7v6dixIx07dmTgwIF06tSJt5e+DV0gzCMMrUZrC0SKe0Yw6RZXfF4p5osvvuDAgQNcdZV1LSw/Pz/WrVtHnz590Ov1tGvXjr179zJ9+vSz/g3A+nc3GAy2v6VerycxMdGWV6miooKswiwu6xyM2pzW26gHCWrsQKZ0C9H69PZ0Y09RGbsLy7g+yPf8B1yEfr16MPjjb9jYJoovTQrnumSZNdYkbONvDyG4f9dz7Flt47CPiOrcxpYgr+ZUarB2i4WEhNiWbDjTwOf/HV1Ez54xvLl6Dlqtlm3btrF9+3aGDRuGl5c38fF/8v3Dixl/7XieWHZLnaRuGq2CRlEoLK3gvUc246JzoaTM2Van3T6jCDz4OTsqrAFRYmIigG1WlaIoPPLIIzz77LP0WvouvU0JfPTpZxzMLuSr8CPw3kjahw7EydkZQ+oBDCcPcsc4b6ZOnc6IESNYv369rS6K2QsVMy5ttShaheJ9xXj390ZRVCryKyhPLSf4lmCKyotrnUNV5uSNGzfyz3/+k6Bb5pG/6WOc/NvgNeBmZn+5m61PeFFgKOBw/mEGhQ7CSeuEqqpkF2UDEO4Rbitvzpw5dAk5iVueBbNZZcGCBRQWFvLwww/z+eefM3z4cF588UU6depkC2hcXFxsg5GPHTvGypUrGT16NIGBgZw8eZKXX34ZV1dXrrnGGox5eXkxbdo0nn32WSIiIoiMjOTVV18FoG+HK+v1/mlOJKixA1nQUojWp6enG5DD38Wll+T1Brk7sxE4EBjKieRUItu2OeN+ZsU6s8jJ4+xdVPZwvoHPer2elStXMm/ePAwGA5GRkUydOpVHH30UnZP2rOV6ezhTEe4KQObJ6qBhV74Pj71bAmwDYOLEiQA8++yzzJ07F7COLykvL+ehhx8hNzcXb29vXnnmETSBJ/njr2+Z//l6dKjowqLJ/uoZxn+rYejQodx9992sX78eY7qRbh5Xs+T2p20zpq7aeAMbVvyI1s2NBWPfIG7pPDpGd+HzmT/QL6KjLXgbPnw43t7e7N+/nxdffJHRY8dxx6zJzD/0I66hvuQHtiO3IJfImyLx6u2FzkeHudhM8cZispKz8GxrnVbvUeRBXFwco0eP5tChQ7y3dB2FheWgQJs2bfjss8/o1q0b06ZN48svv2T8+PHcf//95ObmotFo+PHHH20JC11cXNi8eTMLFy4kLy+P4OBgrrjiCrZu3UpQUPWMvldffRWdTsfkyZMpKytjwIAB/PvmJ3DVezpc8j1FVR0tCfKFKywsxNvbm4KCAlt6a3tYMf9PclKLGf9gL9p28z//AUIIh7e7qJQxfx3CV6dl/+U9Gr2l1mw2E7FhNxaNhlVeMDi2d519TBVm3v2XdRzH3U92w71tSKPWqbFs2p7K3mWHMKEyKW4wAb6uqOYKCp9vhzfF/HXFh/QbcdN5y5k4cSKbNm0iOzubQF9PBvoXctUV/XjJ52n6t/PjGpdEW5daTTUDpQ/++JlHnrqdgu2FOFucGDlyJIsXLyYiIgKAnTt3Mn36dA4ePGgL3iZOnMijjz6Km5sbw4YNwzsymN2hd6Kai8n/7T5Kj5ZiLjaj9dDiGuVK4PhA3Nq7AfBwp4f5Yv4X7NixwzpmyduZUaEGJtzQnhueS7TVsaKigieeeIKPP/7YFogsXLjwjFPwL8TKWR+TXR7OtRM0RI4cZpcyL0Z9r98XNQokLi4ORVGYNWuWbduqVasYM2YMAQEBKIpiG/F9LqtWraJfv374+Pjg7u5O7969+fjjj2vtM3fu3Drptqsi6aYmyfeEaH2i3V3QKda8NicNFec/4CLtTzyCpXKqcfeOHc64T0VJdUp+J0/XRq9TY7m8fxiFzqBD4X//OwaAonXiUKB1QVDdn2/Xq5wVK1Zw6tQpjEYjJzNy+PqR0UQEWlsxUODuu+9GVVVUVeXqT2bQY1kP/u+bl2wBDQBahbDJYcS8MZzS0lK+//57W0AD1pXCt2/fTn5+PmVlZRw8eJC5c+fi5mYNUjZs2MAzL1iXoFB0etK2pZGdnk1WYRbHko+xa/0uvn/wez4e+zFfXPsFdw26izVr1pCRkYHRaOSHJRP49CY3IkNrJy10cnLitddeIyMjg8LCQtatW2e3gKby12PlYM0eF9z9VJVeu2fPnrW2l5SUMGTIECZMmFArNfa5+Pn58dRTTxEdHY2zszM//PADU6ZMISgoqNaAte7du9fKVlkzv0NTMhmtOR7ivz/O3t9SUTQKikJl8IV1kJim+nFVCvKajxUFOO04pfI4TtuvqnzOVZ7mtNevcdyZ63VaWRrF+qY+w3G161X3NU+vi95Nh7tP4w6kFOJS02s0RLu78ndxGf227edKX0+KzGZ2Flq7oyaH+VsH46JSOU4XoHJb9faqbcBp+6m0+XsPMevXolMtHDD7MtMtmhxvP7as3kflPzDA+kOrQIVZAazdCt8vPYxGp0FRQKPVoCgKGo3137NGq1h/Vt7XaDWVPyv/TWus+2o0ClTto9Gg1SnonLXonCv311j3t5WhqyqzRnmVnyU6Jw1aJw0anQatVkGjU9BqrI9Pp9FocOvoBfsLSf27erHJNmMfxvTRt/Qu/5O/1q2k31W3NuyPFtGfNYetAeifx2snWLRN+Djtu6nBYrJu58KvN5oas0jcnNxs9/2pR8u+2RqoqprGWbDyrCp/D47WmXNBQU3N9Nrz58+v9dzkyZMBSEpKqnd5w4YNq/V45syZfPTRR2zZsqVWUKPT6ZpN60xNzq7WX2Pa0YLz7NlKKTBuek/axThOVkoh6qOftzt/Vybr25hXe4Xrj0/lnOmQBnlp5Wd02m9NOJff61/kOYXjlQ1JnL8VJv1Y4UW//qVU9cWrKlhyMppRAffcCl5+apP1S59GwTP/ZTopKag/7mHd9iL0zk5oNCqKBlsgptHW/KlB46RD0enQlAXhZbAQrWqxoPLDmqNodQqKVkGTU0SIJoqTJTvYumcnWq0GrU5DStpR3IxeaM0eHD1ZiItei95Zh95Zg16nxakyEKznWTb496KarTl/VO2lHQJrq6nFcq7dmp0L+i3VTK99elBzsVRVZf369SQmJvLyyy/Xeu7w4cOEhYWh1+sZMGAAL774Iu3btz9rWQaDwTZtDax9co1h7LQYUg/mWadMVk6fVC1q9ZTJqu0WbE2d1imWVfvU2K/GcbbtltqPq7fX3aZaKsuF6vtn2+e0bbXqevo5VJZXp+6W2uWcfg7GMjNmk4XcUyUS1IgW59GoEHp6uKIo4KwoaBWFLXnF+DlpcdFav51XZyOubL2scXxV1uKaGYurro8K4HHLBJhrDWrONrU22icNrQbMqoLZopBa7EO5RY9fmLs1MahqTRBa8/Onalv1Z1P1Z1HV5wdVj6kxJbzmv/+qCtjpi7yqgmpWsZhrF6hFwSPHZHtsoSOJVGZWPtnQV/GjDVA1xPrE6hO2Z8Zxt+3+rj35tvvhdOdOngfgp4S/quuLihmwABbFelMVUBUFS+UfU9UoqBqwqCq3VjhjwYmXnv0MNCpUBmJoAEVF0VIZmCnVwZ1Wg09GFyjyQ3PQzM4ln1c+p7G2umk1tpYxpbI1rup5RaOgaDR1Hltb3zRoNFoUrQ5Fp8HZ3R3PqA5oa+Y/UuouI+EIGhzUnJ5e214KCgoIDw+3JlnSalm8eLFtLj5Y00UvX76czp07k5GRwfz58xk8eDD79u2rM/2wSlxcnG3F1Mbk5e9KtyGO23/dmNZ9uI9Df2Sg0cp4I9Hy+DnpuD2s9ufPDcH2m96t3noTxz9bjuHQIToGFPCXyQKKBj9zOrlaa6t15IAoOt442G6vaW9VX4rMFWbMFSomkwWLRcVismA2qVQYzBhLKygrrqC8xPqzotyMyWgmK7+cXCzgpsViVq3HmVVUYyltSg4S7KZBpyhYzJXLHFms12CLinWbqtQ4zhrIGc0aDpX7U6FxRVF0KBYVRQVVk49CBRpVgw5nNBYtGosGRbX+1JzW/aSgVF9Aa/Yb1j572z2/quMzGtbbUMRIbFfbnQ069CxUwFx5q1qRvhiFU2gVExrMKIqK0RJaY3/H0aCgJiUlhZkzZ7J27VpczpHR8kJ4enqSkJBAcXExv/76K7Nnz6Z9+/a2rqmxY8fa9o2JiWHQoEF06NCBjz76iNmzZ5+xzCeeeKLWc4WFhbUGeInGVxXkSw4fIRpOURR0gYEYDh0iekw3cjakc7w0zBbQaCwVBPbs0sS1PLeqf/s6Zx06Z7Df6LrL7VZSfaiVAZXZbKGiwoLBYKbcYKa8woTBYKas3IShwozRaKGiwkxFhYUKkwVThQWTyUJ2aS46pzzcnS22QEu1WMtTLVRus1gfm6u2q2iNRkIKsvHReqBBVx20WRQsFgW16r6qVLa2KZU3sKDUeFy5Dxrb86qqQUWh3OyKSXXBpFYGXlVpSjDjHXnm1AHNVYOCmqpsibGxsbZtZrOZTZs2VaeyvsDBuxqNho4drU2KvXv35sCBA8TFxdUZb1PF3d2dmJgYDh8+fNYy9Xo9er0MUG1KVU3JMjNMiIZTzWYqTp0CQHFxIaegFCrHi7pW5DF4lD/eHcLPUYKwF0WjoNUoaJ00OLuAu2dT18h+VIuF0rRTmMpKUE1mVFMFFouKa2AwbqGh5y+gGWlQUFOVLbGmKVOmEB0dzWOPPWbX2Ug10zificFg4MCBAwwdOtRurynsr6rpWbqfhGi4vM9XYDx+HI27O+79++Pzww8UGqGN7hTjF99uW01aiIuhaDS4hztWi8zZNCio8fT0tKWsruLu7o6/v79te25uLsnJyZyq/HZxeiprgDvvvJPw8HDi4uIA69iXfv360aFDB4xGI2vWrGH58uW8/XZ1LoI5c+Ywfvx42rZtS2ZmJvPnz6ewsJC77rrrAk9dXArVS0g0cUWEcDAVaWlkLVgAQNCch9H6+NDj6s4kry4m1RTGtzM+w6JaBxRrNNChbyA97x1z7kKFaOHsPkds9erVtTI0nimVdXJycq1vGCUlJUyfPp3U1FRcXV2Jjo7mk08+4dZbq3MQpKamctttt1kzQwYGMnDgQLZv305kZKS9T0HYkcUiLTVCXIiMl17GUlqKa9+++FR+FkZd0582az4h1RRGmhpWvbMFTv0FhuLVXDbruiaqsRBNT5ZJEI3q+7cSSN6Xy8i7uxI90LH6ZoWwl7lz59aZiRkcHEx6ejpgzar+7rvvsmPHDnJycvjzf//D46HZoKpEffcdLl06244zFpdxZewQth/axStTHmdYzEDSjxWxK1fDz38t45ThMOkZ6YSFhXHHHXfw1FNP4ezsfEnPVwh7q+/1Wxa0FI3KNlBY+p9EK3eujOinZ2Iv/N9PeKgq7kOH1gpoAP679B18O4TAIeh03QAuu+F6cv5O4qdHV2BGYdEbb9I1pjt///03U6dOpaSkhNdee+2SnacQTUmCGtGoZKCwEFbnyoh+eib2sl3WhCQeV15Za7/du3ezYMEC4uPjCa0xKyX/aBrd2vanb2gXxl0/HoD27duTmJjI22+/LUGNaDVk6LxoVNJSI4RVVUb0qKgoJk6cyLFjx866r8bdunhh1htvULx5MwClpaXcdtttLFq0qE5w5B0VDIBR64bFZLZtLygowM/Pz96nIkSzJUGNaFRVyfc0kqdGtGJVGdF//vlnli5dSnp6OoMHDyYn58zrQwXOmoVLz55YiopIeWA6FadO8dBDDzF48GCuv/76Ovu7V2Y1tmicqCgsAeDo0aO89dZbTJs2rfFOTIhmRrqfRKOyrXwr3U+iFWtoRnSn4CDaffIxB3v2ApOJ1d9/z/r169m1a9cZyzeVG233FWcnTp06xdVXX82ECRO499577X9CQjRT0lIjGlV191MTV0SIZqQ+GdEr0tKsdxSFjbt2cfToUXx8fNDpdOh01u+jN998M8OGDePkln0A6ExlZOZmM3z4cAYNGsSSJUsa/VyEaE4kqBGNSvLUCFFXVUb00LOkoDeeOMHRMVcD4NK1K0/OncuePXtISEiw3QD+85//8M4bi9i+LgsAT6fjjBg1kr59+/Lhhx9KxmHR6kj3k2hUVRmFZUyNaM3OlxH99Ezs2+fOo015OQE6HV0uu4zgGhnZa2rbti35Px2mxMmfsoIk/rN9AZHto3jttdfIysqy7Xe2WVdCtDQS1IhGpUpLjRDnzYh+eib2mQnWsTOP33knQx9/7KzlWiwW9hx2Ah0UmHdxNOk4R5OO06ZN7XV8WlGOVdHKSVAjGpVM6RYCVqxYcc7nJ40Zw9iXXyH3gw9s2wIfnk3A1KlnPUZVVba9vIo0nTsADz37MM9/8ZZ9KiyEg5KgRjSqqjE1MvtJCCvVbKbol18pWrsWFAVLaSnFmzaByWTbx3fSpHMGNFWOHjaCDtpoT+HXfURjVlsIhyBBjWhUMqZGiNrS584l/8uv6mx3698fv7vuxG3AQLQe7vUqq0jxAeCy23rbsYZCOC4JakSjktlPQlSzlJeT//Uq22Ova6/FpVs33Pr3x7VH9waXp7MYMWqdZcyMEJUkqBGNqqqlRsbUiNbuQHTXWo+77NqJxtX1osp0U0ow4sGRX/cTPjTmosoSoiWQJAaiUVUNFJaWGiGqKU5OUJlDxpCXx4EFr5Hw8EMkvvs2RSnJ9S6nax8vAPad8mPf8l8bpa5COBJpqRGNyjZQWMbUCAGA1zXX4HXNWDR6PUkfLaPg9QU4GyvQA5YffyLlP29S6umOpX073PtdRujYa/Dt3uOMrZ29HxhL6szPSKkIY+MWE4qynm6TZcCwaL0kqBGNSjXLQGEhABQXF9TycgJnz8aAhb8m3Iz73v04A2UuzpjDw9ClZ+BSUoZ7UQns3ge795Hx/jJOuOipaNsG1759CBw1mqBBg9FotWg0Gsa9divfz17BScLZsNmEW0A87cZe1tSnK0STkKBGNCqLKi01QgAoGg0qsO+px9H+tRN3swUVKIrtRc9Fi3H19QOg+EQSaWt+oHDbdjh0GNf8QlzKDbgcOgqHjpK34isydFoMYaE4x/TAs08frvzXAH79TzwZmjD+/ilRghrRaklQIxqVtNQIASajEbPFggK4/PEXAGWe7vg8Modut0ysta9HZDs6PfBPeOCfABhycjj5vx/J37IZ84GDuGbm4Gwy45ycCsmpGH/8iXTAve0YaH8dFRWWS3x2QjQfEtSIRqOqKlUzTaWlRrQWZcVF7P31Z0ry8zCWlWEsLyN1/176W0y4AeVuLrjeMYne/5qJxsnpvOXp/f1pf8edcMedAJjKSjm1bh25G37DeOAAuowsXErLKPRsC4Cbn0zvFq2XBDWi0VRN5wZpqRGtR8LPP7D1i0/rbD/YM5qevfrR818z0bq4XHD5Olc32l53PW2vu962zZCfx29z1gPgFdv5gssWwtFJUCMajVqjFVyR5AGilSgvLgYgtHM07ftchrOrK14BQUT16YdW1zgfuXofX/SeFgxlsG9TKgOut6DRyD860frIu140GkuNLKfS/SRaC4vZDEBkj14MvOlW+o69jo6XDWy0gKbKsEm9UFUzhjJ/vl+4plFfS4jmSoIa0WiqBgmDdD+J1kO1WJsolUvcUtKxX2eiYioASD3kxvdv/nhJX1+I5kCCGtFoLBZpqRGtT1VQo9FoL/lrj/vnNYREFQGQvN+VfZv2XvI6CNGUJKgRjUaV7ifRClks1u6nS91SU+XGR8YD1nE9mcnZTVIHIZqKBDWi0dQaKCwxjWglmqr7qUrGsVOABwAd+7ZvkjoI0VQkqBGNRq2x7pOs0i1aC4ut+6lpPl4NZdZxNapqxjfUr0nqIERTkaBGNJrqxSybuCJCXEq2jJNNE8i37R6JqhpQFC2nDqU2SR2EaCpyuRGNpqqlRiOtNKIVqRpLpjRRNL/rpx0oih5VtRDaMaxJ6iBEU5Hke6LRWCyymKW49NQKC+Yio/WBUnlDqfqv1mM0Clr38y9V0KDXtwU1di22XtKOpLLtuzwURYe7dx6e/t6XvhJCNCEJakSjUSWoEZeYWmEh/dV4zIXGeh/jPigU3+s72rEStgXP7FdmPVnMKmCdSt75stBL/vpCNDXpfhKNpqqlRhLviUvFXGSsDmh0GtApoK28aapuVLbWWBmTi+xah/O11MTFxaEoCrNmzbJtu/vuu1EUpdZt4MCBtY5bsmQJw4YNw8vLC0VRyM/Pr1N2eJcIjmb/wqvfzGD4bSPxdPNi+ODh/Ll6OycTU+x1ikI0W9JSIxpN1ZRuGSgsLpmqgMJZS/hzg8+5a3liLtkf7mu8OpzhjR8fH8+SJUvo2bNnneeuvvpqPvzwQ9tjZ2fnWs+XlpZy9dVXc/XVV/PEE0+c8aW//vprlv36Dtf0nUCHoMcBOJV7jPg1pfz540E69t3P6KlXybpQosWSd7ZoNNL9JC65qnyP9XjLqeff5cKqcJbZT8XFxUyaNImlS5fi6+tb5zi9Xk9ISIjt5udXezr2rFmzePzxx+u04FQxmUzMnDmT115/jU/XL2HU9e0IC/AgtmMvoARF0XJ0lxNLZ66irKjELucqRHMjQY1oNFUf7tL9JC6V6oCi6etwevfTjBkzGDduHKNGjTrjcRs2bCAoKIjOnTszdepUMjMzG/S6O3fu5OTJk2g0Gvr06cOtsyfy3dH3uGJGF+5/cyz+ofkAmCr8+OCRP1j26JeYTKYGn58QzdlFBTVn6htetWoVY8aMISAgAEVRSEhIOG85q1atol+/fvj4+ODu7k7v3r35+OOP6+y3ePFioqKicHFxITY2ls2bN19M9UUjk9lP4lKxWCzk5eVxJOko+7Wp5FYuE9A06nY/rVixgp07dxIXF3fGI8aOHcunn37K+vXref3114mPj2fEiBEYDIZ6v+qxY8cAmDt3Lk8//TQ//PADvr6+XHnllRQWFzLx2Ztq7V9S6M+RPw819OSEaNYueEzN2fqGS0pKGDJkCBMmTGDq1Kn1KsvPz4+nnnqK6OhonJ2d+eGHH5gyZQpBQUGMGTMGgJUrVzJr1iwWL17MkCFDePfddxk7diz79++nbdu2F3oaohFVrdItQY24GBaLhV27dlFRUYGbmxsABoOBkpIS8vLyyMrKIisri4oKayZdnMAbN2IY3ST1Pb21KCUlhZkzZ7J27VpcXFzOeMytt95qu9+jRw/69etHZGQkP/74IzfddNMZjzldVSbjp556iptvvhmADz/8kDZt2vDll18y9Z57UVVLrWArpKPMkBItywUFNTX7hufPn1/rucmTJwOQlJRU7/KGDRtW6/HMmTP56KOP2LJliy2oWbBgAffccw/33nsvAAsXLuTnn3/m7bffPuu3H4PBUOubTmFhYb3rJC6edD8Je0hJSeH7778/734ajcZ2YTdQ0djVOqvTk+/t2LGDzMxMYmNjbfuYzWY2bdrEokWLMBgMaLW1V/QODQ0lMjKSw4cP1/t1Q0OtAUq3bt1s2/R6Pe3btyc5ORmNTotGU4yqetmeP7jtIAOvH9TwkxSimbqg7qfz9Q1fDFVV+fXXX0lMTOSKK64AwGg0smPHDkaPrv3Na/To0WzduvWsZcXFxeHt7W27RURE2L2+4uwsVbOfJKYR9XSmLu2ysjKysrL4/PPPeeWVV3j55Zf59NNPiYiIYMSIEdxyyy14enry66+/8vprrzNv3jzKy8vrlJ2Xl8fkyZNtnwf3PDqNgnL7TucGauSpsf4YOXIke/fuJSEhwXbr168fkyZNIiEhoU5AA5CTk0NKSootUKmP2NhY9Ho9iYmJtm0VFRUkJSURGRkJwOh7uuHinmN7PjAi4AJOUIjmq8EtNVV9w/Hx8XatSEFBAeHh4bZvLYsXL+aqq64CIDs7G7PZTHBwcK1jgoODSU9PP2uZTzzxBLNnz7Y9LiwslMDmErJ9Y5WWGlEPZ+vSTk5O5sMPP2To0KGsWLECb29vDhw4wGWXXUZQUBAAa9euZezYsQzqM4CX33iVM40Uvv3220lNTeWnn34CYOpd9zBr3wt8MnORXc/j9JYaT09PevToUWsfd3d3/P396dGjB8XFxcydO5ebb76Z0NBQkpKSePLJJwkICODGG2+0HZOenk56ejpHjhwBYO/evXh6etK2bVv8/Pzw8vJi2rRpPPvss0RERBAZGcmrr74KwIQJEwDo2K8z7ft25O3p3wOeHNx2hA59O9n1/IVoSg0KaurTN3yhPD09SUhIoLi4mF9//ZXZs2fTvn37Wl1Tp6/0rKrqOVd/1uv16PV6u9ZTNICtpUaCGnFu5+rSXrhwIZ06dWLChAn06dMHgPbt29fap6pl54v3PwNg3svPk19eyMKFCwG44YYbbMHM4MHW/DVdO3bhl6NbOZKRRDB9WLJkCZ999hk7d+6kqKiIvLw8fHx8ar3OoUOHeOSRR/j9998xGo3ExMQwf/58hg8fbtunOqip3/teq9Wyd+9eli9fTn5+PqGhoQwfPpyVK1fi6elp2++dd95h3rx5tsdVLdkffvghd999NwCvvvoqOp2OyZMnU1ZWxoABA1i/fn2tKeQajYa2XXUkH4CUA6X1qqMQjqJB3U81+4Z1Oh06nY6NGzfy5ptvotPpMJvNF14RjYaOHTvSu3dvHn74Yf7xj3/YxsoEBASg1WrrtMpkZmbWab0RzUd1S00TV0Q0e2fr0rZYLGzcuBE/Pz8WLlxIUFAQAwYM4Ntvvz1jOUeOHQUgODCo1vasrCx0Oh1paWm22y+f/IiX3oP447uB6uR2Tz755FnrOW7cOEwmE+vXr2fHjh307t2ba6+9tvZnUz2Cmg0bNtgCLldXV37++WcyMzMxGo2cOHGCZcuW1WlVnjt3Lqqq1rlVBTQATk5OvPbaa2RkZFBYWMi6devo3r17ndf3DbGOqzGbfPlx0Zqz1lMIR9Ogy82F9A1fKFVVbYN8nZ2diY2NZd26dbX2Wbdune1bl2h+qnOQSUuNOLtzTXfOzMyktLSU33//ne7du7N27VpuvPFGbrrpJjZu3Fhr3+LiYt56778AdVqSy8rK6ia38/HF382HrMJs4PzJ7bKzszly5AiPP/44PXv2pFOnTrz00kuUlpayb191ZuKzJd9rTrpfWR3oZKVIa41oORrU/XS+vmGA3NxckpOTOXXqFIBt0FrVBwnAnXfeSXh4uO1DLC4ujn79+tGhQweMRiNr1qxh+fLlvP3227bXmT17NpMnT6Zfv34MGjSIJUuWkJyczLRp0y7w1EVja8rVioVjOF+XdtVspi5dujBmzBh69+5N79692bp1K++88w5XXnmlbd8ZM2bQJ6Y3//v15zO+VllZGUFBQfj4+HDllVfy9N1zUFHPuJzBmfj7+9O1a1eWL19O37590ev1vPvuuwQHB9ea2WR739f7t3DpZZ3IsN0vKfDj4Nb9RA/udo4jhHAMdl/7afXq1UyZMsX2eOLEiQA8++yzzJ07F7AO/Ku59khJSQnTp08nNTUVV1dXoqOj+eSTT2rlbrj11lvJycnhueeeIy0tjR49erBmzRrbqH7R/FQvVtycP95FUzrfdOeSkhJ0Oh2BgYG1Wvy6du3Kli1bbI+rWnvm3DfrjEFN//79OXToEOvXr+f48eP8+9//5poNN5BTmk+gl3+96qooCuvWreP666/H09MTjUZDcHAwP/30U+2xN1Vv/Ga8vlJkTHu02gOYzT4A/PmDBDWiZbjooGbDhg21Ht999921+njrc8z8+fPrDA48k+nTpzN9+vQG1lA0FdvaTxLTiLOo6tKuacqUKURHR/PYY4+h1+vp3r07OTk5tfY5dOiQ7QtNzdaev7dZx8cop7WTzJw5k3fffZfS0lLGjx9Pv379iIiIwGw2c1mHXvWqq6qqTJ8+naCgIDZv3oyrqyvvvfce1157LfHx8bbp147QUqN309N3TBjxa6xdT0GRnuc5QgjHIKt0i0bT0FkgovWpT5f2lClTmD17Nhs3bmTo0KH89NNPfP/997YvR1WtPX379rW955JSTvDGG2/w1ltvYTQa6dq1K1dffTVTp07l3XffBUCr0RDlHU4H3wjUCrMt+q7KhH269evX88MPP5CXl4eXl3Wg7eLFi1m3bh0fffQRjz9uXRXbNlC4GbfUAJQVVefyyU8v4au4b/H0d2P4XcNw1juf40ghmi8JakSjUSundDfrr6zikoqLi+PJJ59k5syZttk/AAcOHOCxxx5j48aNlJSUkJiYyOzZs2nbti0jR46kR48efPbZZ3z00UcAfPLJJ1x++eWAtbXngQceqDUGr8pdd91lm8Dw6aef8uCDDzJ69GhUVcVkNvN//f5BRXopJ/9dncQzO9nacnRy7laKXT2t71+NQvKhv0CFtOe3U+hsXa5B0Wqw5BnJ/zWJU6btoEBs2Qiiw/qg1M3/16z0v74/B7b9D7PJl5w0HwAyTsDxPb9y739Go3Oy38QPIS6V5v1VQjg0aakRNZ0tud7Ro0e5/PLLiY6OZsOGDRw6dIj33nvPNnBYVVVCQ0O5/vrrbZMLxo0bZzve09OTxYsX07FjR64cNJRp06YRER5Bz549WblyJUePHmXOnDkkJiYyf/58Vq9eTUxMDKEhodw8sLqczOIc9mUcJinvJAAHs46xL/0weSWFYFKJDemGt4snD61+gf1phzmWlczzP79FSt4pRkQMwFJcgaWoAj0u+OlD0eY27/e9q4cb9y64np7Ddbh55tq2m016ju9ObcKaCXHhpKVGNB7bQOGmrYZoeudKrvfUU09xzTXX8Morr1BScozDR14gLNxMWXkaeXkxlJWlcsWVMfj5diA6OvqM5VdNt5730NMcyjyOs5MTQ4YMYc+ePSQmJp41uV2b8DaoJguosPT5eTz33+dtZf7js38B8P5/l3Dn7SMJUWHNjT/yzPy53PbtHCpMFXTr0pWvP/6C4SNGW3udVJWTb/6BE84O0UCpc9Yx9NYrOPTHl7W2F2bKOnnCMUlQIxqNtNSIKjWT69UMaiwWCz/++COPPvooo0dfRXz8BkJCtNx2mw9DLt9s22/AAEg+EQOcOaipmm797U+r6dijC9PvvB+Lp3V20uDBg7nmmmvOWjfF2drNMm/+c8yb/9w5z2Pg8CGsHb7unPuUKoV4qwGoDjLrz2gwUlbijaKARlNIWCcdfa6um7BPCEcgQY1oNA6Qg0xcAudaLy4zM5Pi4mJeeukl/v3vB7n5HweIjy9j7txMliwdS7du5ZSUpKPRlOPhmXOG0q2qpluPGTmaL79fhUajISQkpO5060vBwZJOlhWUoijWS0GPK/3oOji6VsoNIRyJBDWi0ciClqK+yfVGjerBsOEJlJTo6dGjPZkZPvz6izv33vMj27cvoKT0v7i5uWMyGQH4feuLjL06Do3G2spSNd3a18ObKVOm4K/1osTJWGe69aVR+X53kKDGO8gHJ+dcKox+7PnNxO71e+k2WGHEXSOaumpCNJiE46LRqLKgZat3vvXi/P390ek0+PkfpqTkEAAB/iPo2rUrycnJlaVUTbE2Ef/X09Z7pi/YsuUZ2+tUTbeOu/8Z2rZtS9vQNixevBhXV1fbjKlLpSpHjiO97W99ZhTu3tbBwoqikLwv9zxHCNE8SVAjGo0skyDOt16cXq+nWzdfUlMqAGjT5k46dHikVnI9tTI6dnE5jrNz9aycCtMKfv75TipMBkpLrUnknLz0lc9a33QajcbWGnTJOVALpXeAD3e//A88/azBjKEMyoplTSjheCSoEY2n6gu2RDWtVlVyvZq305PrTbwtlA0bitm75xa0mkm8886HfP/997bs4SEhA8jNNXHkiIGU5MrkescHcOSIgdKyTWzb+jKDBg3C19eXp/87n/T0dLIKc3jkkUc4fvx4renfl5IjtlD2vqoTqmrGVOHHp//+EZPJ1NRVEqJBJKgRjUZaakR9jBzZgZmzAnjzzeXExMTw3nvv8fXXX9uS60VFXUHCrluZdv9J3ngjHYBZs75g2v0n2ba1lNLSowQEBPDTTz9RWlrK8uXLefWdBWzZsoXvvvuOXr3qtwyCvSiqY42pqSm4XRCBbYoBMJT58+e325u4RkI0jAwUFo1GFrQUZ3L62m/Ozn6MHevFAw/8kw7tHzrjMS+88AovvPAKAGVlRfy+tbctZmgTfhsA/fr1452X3uLrLT8QrPPlgadnNto5nIvJaAQ9GMsuffeNxWLBYrJgNpkxV5gwm83V900WzBUmLGaz9b7JjGpR0Wg1aHVaTh46xY6fygBvW3nFedIFJRyLBDWi0ciClo1DVVUsFhWLWUU1W++rFhVVrW4dsw5DqbFNrQoyVdsAblU9y/MqlY+r71vMFsxm1XrBtP20YDGpmE0WLObaP82myufM1T9RQe+mw9lVV+N1VY4f9wX1RjL3nCLRa4X1OVsdrf9Tq+pX+bO4+C5QobzckxSthq3qWlRVpTC/EE+1KxbVie+e3lrrnGqfq0rlU9btNc+byn2qXr/m74oa9aJGOVD9vMUPtbwC48+lrFv3fdWzNf+C1hE/yunbK5+r8e/F+joKqErlUdb7VN5X0VTet/5U7JrpsojLxg+1Y3lCND4JakSjqc5T0/hRjWqpvtBbLNaLvdlssW4319h+2mOL2WLbv3qbisVisQUMltOeUy21j69T5mkBh8VsOcNxp9fJco7XUGvU0WL7vbYc1WNe6j/nJth2z1BjqxP+OFXeTzU08eJLGrezPqXa/neG587391XOePe8rAOuVaDqp4qCaguWFMW6iKWiFDJicmciew7E1ePs5yBEcyRBjWg0VS01mclF/PbpweoLfWUgUTsAOc/FvVbAcFrAYVHPeoFodZTK7/CK9Y7tJ4BGqXzO+j9FqdpfsS1lUXN/W7ehQmUXhQatTqm8f/rPyvs6DVpt3Z9aJw2qCsZSE8ZyE0qN18/MWoPRmImnZ3RlnbH9rFlXpcZzKNalF8zmUDSKN4pGsZWpFlcQ6OKLq7MriqbqOMV6/v/f3v3HVFX3cQB/n3O4otwEu/y4xOCppmYsU5Rny+gHhMZyISy2QHOuZTWasyjnRhQT/aPcnA3b/Mct/QOrYbjRbJQNwi0thojgvNRAkkY2CCYKqPdB8X6ePy5cIUB+nXMPHN6v7Qzv99779ft5e+B8vPccrgqog39WFGDgtqIO/FkdWJeq+jJTVfjmhzZkLarqHVe98yuqNzgRD27877rvp6vI4CtpA19x77bvVSjPvWZVPALPwA1NU6FqKtQADdrA20SqpkDVNGg2DaqmImDgqxYQMDCmeccCNGgBAQiwBUANUPkL9WhOYFNDhrHN9/5itJ5ON37rdPt/AQq8BwB1YNNUKIO3h37VvAeke7e9B6vB+9Qh9917vuo9AI7yGN98A48ZPjb4+LGf/++/Y/Tne29rmuo9qA4edGelZWYvwABRZi+AaE5iU0OGWfpfJ267+9Hn7od2nwO95juQq8MP2trAfeooB/cRc4xsWHiCMhHR3MKmhgxjC9QQt+4/Zi+DiIjmCL7JSkRERJbApoaIiIgsgU0NERERWQKbGiIiIrIENjVERERkCWxqiIiIyBLY1BAREZElsKkhIiIiS2BTQ0RERJbApoaIiIgsgU0NERERWQKbGiIiIrIENjVERERkCXPqU7pFBADQ09Nj8kqIiIhoogaP24PH8bHMqabm6tWrAICYmBiTV0JEREST1dvbi5CQkDHvn1NNjcPhAAC0trbeNxTy6unpQUxMDP766y8EBwebvZwZj3lNHLOaHOY1ccxqcmZLXiKC3t5eREVF3fdxc6qpUVXvKUQhISEz+h9vpgkODmZek8C8Jo5ZTQ7zmjhmNTmzIa+JvBjBE4WJiIjIEtjUEBERkSXMqaYmMDAQBQUFCAwMNHspswLzmhzmNXHManKY18Qxq8mxWl6KjHd9FBEREdEsMKdeqSEiIiLrYlNDRERElsCmhoiIiCyBTQ0RERFZApsaIiIisgTLNTVNTU1IT09HWFgYgoOD8cwzz+DUqVO++y9cuIBNmzYhJiYGCxYsQGxsLD7//PNx5+3r68O7776LsLAw2O12pKWl4cqVK0aWYrjxsgKAnJwcxMfHIzAwEHFxcROa948//sArr7yC8PBwBAcHIzMzE//8848BFfiXUXm1t7djy5YtiIyMhN1ux+rVq3H8+HEDKvAfI7L6888/oSjKqFtJSYlBlfiHUfsWAFRVVSE5ORl2ux2LFi1CUlIS3G63zhX4l1F5JSUljdi3Nm7caEAF/mPkvgV4P75g/fr1UBQF3377rX4LnyLLNTUvv/wy+vv7UVlZidraWsTFxSE1NRXt7e0AgNraWoSHh+PLL79EQ0MDPv74Y+Tl5eHgwYP3nff9999HaWkpiouLcebMGdy4cQOpqam4e/euP8oyxHhZAd4dduvWrcjKyprQnDdv3kRKSgoURUFlZSV++eUX3L59Gxs2bIDH4zGqFL8wIi8A2LJlCxobG3HixAlcvHgRGRkZyMrKQl1dnRFl+IURWcXExKCtrW3YtmfPHtjtdqxfv96oUvzCqH2rqqoKL730ElJSUnD27FnU1NRg+/btvo+Mma2MygsA3n777WH72KFDh/Revl8ZmRUAHDhwAIqi6Lnk6REL6ezsFADy888/+8Z6enoEgFRUVIz5vG3btskLL7ww5v3Xr18Xm80mxcXFvrG///5bVFWVkydP6rN4P5tsVgUFBbJy5cpx5/3xxx9FVVXp7u72jXV1dQkAKS8v12XtZjAqLxERu90uRUVFw8YcDod88cUX01qzWYzM6t/i4uJk69atU13qjGBkXk899ZTk5+frtdQZwci8EhMTJScnR6eVms/o78X6+nqJjo6WtrY2ASClpaU6rHp6Zne7/i+hoaGIjY1FUVERbt68if7+fhw6dAhOpxPx8fFjPq+7u9v3Cd6jqa2txZ07d5CSkuIbi4qKwvLly/Hrr7/qWoO/TDWr8fT19UFRlGG/nXL+/PlQVRVnzpzRY+mmMCovAHj22Wdx7NgxdHV1wePxoLi4GH19fUhKStJn8X5mZFZD1dbWor6+Hm+++aZuc5rBqLw6OjpQXV2NiIgIJCQkwOl0IjExcVZ/HwLG719fffUVwsLC8MQTT2Dnzp3o7e3VYdXmMDKrW7duYdOmTTh48CAiIyN1WvH0WepTuhVFQXl5OdLT07Fw4UKoqgqn04mTJ09i0aJFoz6nqqoK33zzDcrKysact729HfPmzcODDz44bNzpdA57CW82mUpWE7FmzRrY7Xbk5ubi008/hYggNzcXHo8HbW1t+hXgZ0blBQDHjh1DVlYWQkNDERAQgKCgIJSWlmLx4sX6LN7PjMxqqMOHDyM2NhYJCQm6zWkGo/K6fPkyAGD37t3Yv38/4uLiUFRUhLVr18LlcmHp0qU6VeBfRu5fmzdvxqOPPorIyEi4XC7k5eXhwoULKC8v12fxfmZkVh988AESEhKQnp6uz2J1Miteqdm9e/eYJwgObufOnYOIYNu2bYiIiMDp06dx9uxZpKenIzU1ddQDakNDA9LT07Fr1y68+OKLk16XiMys9xJhXFYTFR4ejpKSEnz33Xd44IEHEBISgu7ubqxevRqapulYqT7MzgsA8vPzce3aNVRUVODcuXPYsWMHXn31VVy8eFGnKvUxE7Ia5Ha78fXXX8/oV2nMzmvwHLbs7Gy88cYbWLVqFQoLC7Fs2TIcOXJErzJ1Y3ZegPd8mnXr1mH58uXYuHEjjh8/joqKCpw/f16nKvVhdlYnTpxAZWUlDhw4oF9RejHtja9J6OzslN9///2+m9vtloqKihHnc4iILFmyRPbu3TtsrKGhQSIiIuSjjz4a9+//6aefBIB0dXUNG1+xYoXs2rVr+gXqyIisRKZ23kNnZ6dcu3ZNREScTqfs27dvqmUZxuy8mpubBYC4XK5h42vXrpXs7Oxp1aY3s7MaqqioSGw2m3R0dEynJEOZndfly5cFgBw9enTYeGZmprz22mvTqs0IZuc1Go/HM+J8ypnA7KxycnJEURTRNM23ARBVVSUxMVGnKqdmVrz9FBYWhrCwsHEfd+vWLQAYcWa/qqrDrrxpaGhAcnIyXn/9dXzyySfjzhsfHw+bzYby8nJkZmYCANra2uByubBv377JlGI4vbOa7loAoLKyEh0dHUhLS9NlXj2ZnddY82qaNuOuFjM7q6EOHz6MtLQ0hIeH6zKfEczO65FHHkFUVBQaGxuHjTc1Nc3Iq8XMzms0DQ0NuHPnDh566CFd550us7P68MMP8dZbbw0be/LJJ1FYWIgNGzZMeV5dmNpS6ayzs1NCQ0MlIyND6uvrpbGxUXbu3Ck2m03q6+tFRMTlckl4eLhs3rxZ2trafNvQ//FduXJFli1bJtXV1b6xd955R6Kjo6WiokLOnz8vycnJsnLlSunv7/d7nXqYSFYiIpcuXZK6ujrJzs6Wxx57TOrq6qSurk76+vpEZPSsjhw5IlVVVdLc3CxHjx4Vh8MhO3bs8HuNejIqr9u3b8uSJUvkueeek+rqamlubpb9+/eLoihSVlZmSq3TZeS+Nfg8RVHkhx9+8GtdRjEyr8LCQgkODpaSkhK5dOmS5Ofny/z586W5udnvderFqLyam5tlz549UlNTIy0tLVJWViaPP/64rFq1ij/nx/heHAoz5OonSzU1IiI1NTWSkpIiDodDFi5cKGvWrJHvv//ed39BQYEAGLE9/PDDvse0tLQIADl16pRvzO12y/bt28XhcMiCBQskNTVVWltb/ViZ/sbLSsR7ieNoebW0tIjI6Fnl5uaK0+kUm80mS5culc8++0w8Ho8fKzOGUXk1NTVJRkaGRERESFBQkKxYsWLEJd6zjVFZiYjk5eVJdHS03L1710/VGM/IvPbu3SvR0dESFBQkTz/9tJw+fdpPVRnHiLxaW1vl+eefF4fDIfPmzZPFixfLe++9J1evXvVzdfoyct8aaqY0NYqIiDGvARERERH5z6y4+omIiIhoPGxqiIiIyBLY1BAREZElsKkhIiIiS2BTQ0RERJbApoaIiIgsgU0NERERWQKbGiIiIrIENjVERERkCWxqiIiIyBLY1BAREZEl/B/ZkzPKt2xv9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"The second option is Cuy-Summ, Trum-Asht, Asht-Gea, Gea-Por-Stark, which necessitates a vertical slice of Por\")\n",
    "print(\"These are county pairs 17-76, 77-3, 3-27, 27-66-75 (with 27 cut along SW-NE diag or E-W)\")\n",
    "print(\"To start option B - ID Cuyahoga's extreme Summit straddlers\")\n",
    "mC, ppC = 17, 76 #Cuyahoga, Summit\n",
    "borderingSummit = list()\n",
    "for v in countyTractList[mC]:\n",
    "    for vv in neighborList[v]:\n",
    "        if vv in countyTractList[ppC]:\n",
    "            borderingSummit.append(v)\n",
    "            break\n",
    "for v in borderingSummit:\n",
    "    plotPoly(tractGeom[v])\n",
    "    plotCenter(v,tractGeom[v])\n",
    "plotPoly(countyGeom[mC])\n",
    "#plotPoly(countyGeom[ppC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "53843c0f-4893-4826-9d04-eb7cf653c4f5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "checking how much we could shade down straddler pop from natural modulo if whole-county districts are 1.01*aDP\n",
      "540428.0 63682.62626262626 127911.26767676767 136636.19191919192 4.534 10.612\n",
      "58915.17252525251 61034.93131313127 1.006408120108669 Summ & Cuya partials, tgt/aDP\n"
     ]
    }
   ],
   "source": [
    "print(\"checking how much we could shade down straddler pop from natural modulo if whole-county districts are 1.01*aDP\")\n",
    "print(countyPop[ppC], countyPop[ppC]%aDP, tgtPop, naturalTgtPop, r3(countyPop[ppC]/aDP), r3(countyPop[mC]/aDP))\n",
    "print(countyPop[ppC]%(1.01*aDP), countyPop[mC]%(1.01*aDP), (countyPop[ppC]%(1.01*aDP) + countyPop[mC]%(1.01*aDP))/aDP,\"Summ & Cuya partials, tgt/aDP\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "45c4b7a3-50ee-4555-91f2-7d7faa956104",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here are two Summit-pairing choices for optionB, based on swelling in each SE corner. Model off optionA SummPortStark\n",
      "The tgtPop for Cuya-Summ will be 119950.10383838377 vs usual 119186.34343434343\n",
      "POINT (-81.57057476910849 41.321037282749415) 0 5195 cTcP, target_pCP, diToler\n",
      "here is a GOOD Cuya-Summ straddler for nSplits, ppC, mC skew ratios of 0 0.5 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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H22vw+NtG7zisKtOGpIcDlAxOKJIARYhDER9J+U0SsAgh+l1pfQvvbq7i3c1VrNhbH1NNnZPsYO64XM4em8tJIzIlQBHiMIhUrLQfBTfQScAihOgX+xtaeGl1GW9trGBrZXPMujG5yZw9Lpe543KZVJgaN7PJChEvjHDEEk9/WhKwCCGOGn9IY8mmKv69qpRPdtZG7/IsqsKMoemcPS6Ps8fmMjgzsX8LKsQxLlKJqUoNixBCtGlsCfDs8hKe+mwvNe2GHp80PJNLpw1i7tgcGWosxFFkED+dbSMkYBFCHDH76rz8/ZM9/GfVflqDGmD2SbliRhGXTyuSmhQh+km0hiWO2oQkYBFCHFaabvDx9mqeW17C+1uro80+Y/NTuPHUYVwwqeCgGWWFEEeW9GERQhy36jx+Fq0s5bnlJTHz9ZwxJpsbTx3OySMy42pEghDHsugooTga2CwBixDikGwqb+KpT/fyypflBEI6AKkJNi6bNoirZg5mZE5SP5dQCHEgXWpYjm313gBef4iiDGl3F8e3kKazZHMVT326lxV766PLJxam8s2Th3LBpHzJlyLEAKZLHpZjV0sgxDl/+JhaT4DBGYmcMiqLmUMzmDo4naKMhLj6pQvRF7pu8OX+RpZsruKVtWWUN5lp8q2qwlcm5nP9yUOZOjhN/haEGOCMdun44+nPVQKWHtpa2UytJwBASX0Lzy0v4bnlJQBkuuxMGZzGlPCMsCcMSsPlkK9WxD/DMNhW1cyPX1zP+v1NMesyXXauPnEw15w4hLxUmWBQiHjRfvqg4yYPy8KFC7nzzjtZsGABDz30EACLFy/mscceY/Xq1dTV1bF27VomT57c7XGeeuopbrjhhg7LW1tbcToHxolwezgT58xhGfzvacP5dGcda0oa2FTeRJ03wHtbqnlvSzVgtgmOyUsxg5iiNKYOSWdYpiuuho+J45emG6wtaeCdTZUs2VzFvrqWmPUXTMrnnPF5nDMuV5p9hIhD7TOwxNNlqc8By8qVK3n88ceZNGlSzHKv18vs2bO5/PLLufHGG3t8vJSUFLZt2xazbKAEKwA7qz0ATChIZc7YXOaMzQXAF9TYXOFmbUkja0oaWFfSSFljK1sq3GypcEdrYVITbEwuSmNqpBamKI3UBFu/fR4h2vMFzVmR39lUyXtbqqK1iWDOiHzisAxOG5XNdScPwWGVIEWIeKa3bxI61kcJeTwerrnmGp544gnuvffemHXXXnstAHv37u3VMRVFIS8vry/FOSoiwzSLMhJiljttFqYOTmfq4HS+zTAAqtw+1pY0sLakkbUljawva6SpNcjH22v4eHsNYLYbjsxOijYljS9IITfFSYbLjs0iOSr6g2EY+II6zb4gbl+IZl+QZl+IkK73+lgKChkuO/lpTrJcjgFXu6brBqUNLawrNfukfLS1Gm9Ai65PcVqZMzaXc8blctrobGniFOIYEhOwxNHlpk9nofnz5zNv3jzmzp3bIWDpK4/Hw5AhQ9A0jcmTJ/OrX/2KKVOmdLm93+/H729L8e12uw9LOboSCVgK0xIOsiXkpjg5b0I+503IByCo6WytaGZtaQNr9jWwtrSRfXUt7Kj2sKPaw79X7Y/ZP8NlJzvJQVZy+DHJQXZy22PkJyPRPuAuhAOJYRg0tgSpbvZT5fZR3eynutlHtdt8rPcGaPaFwj+R4OTwp6u2W1TyUp0UpDkpSE2gIC2B3FQn+SlO8lLNnyP1uzQMgxqPn+2VHrZWutlW2cz2qma2V3mimWcjclMcnDMuj3PH53Hi8AwJnIU4Rh03fVgWLVrEmjVrWLly5WErRHFxMU899RQTJ07E7Xbzxz/+kdmzZ/Pll18yatSoTvdZuHAh99xzz2Erw8GUNYQDlvSDBywHsllUJg5KZeKgVK47aShgJtlaW9IYDmIa2VXjoc4bQNMN6r0B6r0BtlV1f1yrqpCd7CAnxUlOsoPcFAe5yU5yU5xkR587SD+GAhtfUKPW46feG6DOG6DOE6De66fOY76u9wao8/ip9QSoafYT0HpfO6IqkOy0key0kuSw4uhDVlbNMKhtDlDd7COg6ZTUt1BS39Ll9jaLQk6yk/xUZzSYyU9LYEhGIkOzEhmUnnjQ/iIefygakGyrbGZrpZvtVR7qvYFOt7dbVUbnJnHaqGzOGZ8nsyILcZxoH7DE0198rwKW0tJSFixYwJIlSw5r/5JZs2Yxa9as6OvZs2czdepU/vznP/OnP/2p033uuOMObr311uhrt9tNUVHRYStTe60BjbrwSX9Q2uHJwZKZ5GDuuFzmjsuNLtN1g4aWADUeP7XNAWo8Pmqa2y6+tR4/Nc3mT31LgJBuUNHkoyI8vLQrVlUhJyawMQOZ2NdO0hNtR31IqqYb1Hr8VDb5aGwN0tQaxN0apN4baFcr4qcuHKS0BLSDH/QAaYk2cpOd5KQ4yAk/5iY7yEhykOK0kuy0RR+TnVYS7ZbD9j0ENZ0qt/k7Km9spayxlYpGH5VuH5VN5mOtx09QMygLr++MokB+ipN0l50kh1nWJIeFlXsbKM5LZmtlc7f7Ds10MSY3mTF5bT9DM11YJEAR4rjTvknomK1hWb16NdXV1UybNi26TNM0li5dysMPP4zf78diOfQOeaqqMmPGDHbs2NHlNg6HA4fDccjv1RPlTeaFIMlhJSXhyLXlq6pCZpKDzCQHHKQ7T1DTqfX4qXabzR1VzX5q3D6q3H6qms3HmmYftR4zsClv8kXzZnQlcpdvXtjbAplIsBOpwUnrYWBjGAbu1hD7G1sobzQv2JGLdmU40Kpy+3rdDGO3qGS47GS47GQm2cl02clwOdo9t5OZZH6G7GRHv45ksVlUBqWbNSRdCWo61c1+KptaqWzyU9HUSpXbR1ljK/vqWthX14LHH+ryd9g+UMlJdjAmL5nivGRG5yZTnJfCyJwkEuzSUVYIYWp/xo2jeKV3AcucOXPYsGFDzLIbbriB4uJibr/99sMSrIB5oVu3bh0TJ048LMc7VM2+EGCO9BkoSbFsFpX81ATyU7tvogpqOjXhWooqt4/qcK1FVTi4qW72U+32UecNHPQuP8JuUckON0HlJLfV1oB58SxraI0GJ94e1IhYwjVAaYl2UhOspCbYSE+0R2uAIv13Ml12MpLsJDusA+b3cDjYLCqFaQld9o8yDIM6b4CS+hbcrWZfG48/xCc7aslMsjMiO8msNclNJt1lP8qlF0LEG/14SByXnJzMhAkTYpa5XC4yMzOjy+vr6ykpKaG8vBwgOlQ5Ly8vOgrouuuuo7CwkIULFwJwzz33MGvWLEaNGoXb7eZPf/oT69at4y9/+cuhfbrDxB/unOiwxV8nRJtFpSDN7OjZnUDIrLGJBDI14VqaSO1NJNCp9wYIaHqPApuITJedwvSEaIfTgjSnGWyFO6FmJzukaaIbiqKQFe583d5VMwf3U4mEEPHMaNe175htEuqJV199NSYJ3JVXXgnA3XffzS9+8QsASkpKUNW2i39jYyM33XQTlZWVpKamMmXKFJYuXcrMmTMPd/H6xBee0M15DOefsFt7HtjUeDqvrTEMs1NyYZqTwrREc1RMWoIkFxNCiAGkfaoGaxzdLCpG+0kF4pjb7SY1NZWmpiZSUlIO67GXbKrkpn+tZnJRGi/Pn31Yjy2EEEIcTVVuHyfe9z4WVWHXfef3d3F6fP2OvzaOfqCFO4XaLPETiQohhBCdCYbTPcRT7QpIwNIjWrgSSvpZCCGEiHchzbymScByDIrUsFhV+bqEEELEt0gfFmucZbOOr9L2k0g0KjUsQggh4l1Qi89uDhKw9ECkhkUCFiGEEPEuEB75apcalmNPSAIWIYQQx4hIp1t7H+ZJ60/xVdp+Eu10G0cJdoQQQojORGpY4m1G9vgqbT+J50y3QgghRHsBqWE5dvnD0agjzn65QgghxIGkhuUY5gvXsEiKeSGEEPEuMkpIaliOQRKwCCGEOFZEO91KDcuxJ16HgAkhhBAHil7TpIbl2COp+YUQQhwrIp1uJXHcMUgSxwkhhDhWBDXpdHvMkoBFCCHEsSIy3Uy8dXOIr9L2k3AwKgGLEEKIuBdpErLGWZOQtb8LEA90yXQrhBDiGNE2oa9KSNMxgPBlDgPzSeR1++dOm4rSj9dBCVh6IDKXkCo1LEKIA4R0A4tCv57IheiNSB+W51eU8PyKkh7vN3VwGi9+5+R+uxZKwNIDejhgsUrAIoQAArrOT7bv5+XqRlo1ndlpSZyVmcL52am8V+dmY3MrE5MTyHfY0A0IGQaaYRAywqMOFbApCroBOgboBpNe3IvLaiF7Zj6oChg6Ss1mnOllqJYA6BroIbDYYcKlkJjR31+DiFOTi9KwWZRoArmeWlPSSLM/RGqC7QiVrHsSsPSAJjUsQogwn6bzbEUdz1XUR5d90ujhk0YPv9xVHl22qLLnx7TpBp9XtALQULqj3Ro7LstW0m1/id2hdjuc/9u+FF8I5o7L5cu7z4nmY1FQIHx5i1QUKsTWGk64+x2grXamP0jA0gMhqWER4rizZMkS1q9fz1lnncXUqVOjy5+rqOOuHWUx2/5qZCHv1jXxSYOHA0/niarKlJRErIqCqph94YxwrYuqgIqCRTMADwBKXiL2FAf+7Q0AaEYmFF8AqhU2v4yugNe3hzVLp6BpPpzOAmZMfwmbLe0IfhviWJNot5Jo7/n2dqtKIKRHg5z+IAFLD0inWyGOH4Zh8Oqrr7J27VoAPv/885iAZU5mCoQDlnMyU7imIJNzs1L5n0FZVAVCnPjFZvx6W1V7i66zxu3lK1mp/HREAQXOzq8SJf8uRwVaLhlOUlYiPLkIf/lwfPpMKrYPBUOhISeJiuK3gbUQMvdrbd3Lsk9mYrdno6p2LBYXqmIjNW06GeknYRg6SUmjSUgYfCS+LnGcsFvMgEVqWAY4aRIS4vhRX18fDVYAampqeOCBBzAMg5Bh4DZAOfFcDEVhSZ2bJXVugHAtCWjhoaLFpX6GVYVYOdpJbYqF197bzZv/2kRimoOpY7L4+oR8LhiRjaqa2SV0BVQDEv66kWYAhkfLoLXkANCQv63TMhuGht8f2wblbl5PaemTZtksSZx6yhdYLAmH/P2I41MkK67UsAxw/pA5+aEjzuZdEEL03s6dOzssa2lpiT5PADK8TdQlpcVso7XLaaHoBjN3+BlSEyKrIciiPB1LuI9KS6OfT5aX8cnyMn4AjCzO5KUrp7F9UALjSlu7LVvmrospm/pQzLI6tQhrYjEYOknej9CUROxGc2zZNA8rV32NxMRhDB/+Q5Jcow7+RQjRjiUcWGtG7zrqHk4SsPSAL2hGlDJbsxDHNp/Px8cffwzAvHnzGDt2LB6P2bfktpdeY1SN2RR06eqPGJT/FYIuG1gV3MFqqktqcAa9hKxe7EGF7MZhQApD63Um+gJUWRSak63MmpTD0i/a+sDs3FrH71bs5Zfzp6NpOroO/qCP0pp9jMgfjt3uaFfCUynm+wA8s+Y+8hv/TqZeCp7S6BaWA4KVCK93O17vdhKcgxg16s7D+K2J40GkgUHTJWAZ0HxBs4bFaZMaFiGOZStWrKClpYXMzEymTp2KxWIhKSkJgMRJU2j+zENyaxMqcPqeZhKxs8T2JTWWOlLDx3CEAAUaM74kyT0CZ2s+81rMfitNAfjJRZOYVe6muqQtsLhiXD4AIc3Pa+s+4lcNDqrsGSTsWMvkYDlnJOhcUjyFIXkjovtMGXwx69wfkaA34KAFHZXahFOwBCqwpJxEXsPjnX5GRZEbL9F7kUzv/VjBIgFLT/hDUsMixPEg0hx04oknYrHE/r0/cep0fCdN5v577wVgT24INRiitKUuuk3aSacyNC2DPR8sp8lfiSd1JyGbhyT3SBRUUv3wzGd7uWJ8Pn/zBmmt8wEw78GPUZwWdMBQVUKjEyAXWi1OPrcM53MDFm5pZtraVxjtSuT0ERNQbYW4xr6AAQQw74AjmVkUFBrzbqC0aQcTy6+L+Ry6HjgSX52IIy2BEJ/sqCWg6eiG2dHcMMwBJnr40Yh5Di0B88ZdalgGuMgoIelzK8SxLRAwL+a7du1i2rRpHYIWp9WKaijoisEXTV/ErJtVPIF0GxjeOnJnjuCV9xtx2n34EisJWGpwNUxhr83Oa69vAaBAqaOVzOj+hk8zc18AlspW/jS8kXE5g/hs/y7eadb41JrNBr2aLXX7+E+rh5B9WA8+kYv7GMIQ9kWX2B05ffpuxLHjnlc388Kq0oNv2In+nH9IApYeiEzBHQj1Y12YEKLP3LXVrH/vHQxDp2B0MUNPmIbF2vH0d8YZZ7Bo0SK2bdvGa6+9xiWXXNJhm8l5Y1lTtbnD8i+2boStba/bj17WHRr1lrf5wHEqYDYxlRuZdOUf4/3MmX4uX256nqbKF6F5N5lq2+gMm38HY0ffi6ooqIBBOGMubVX2kXUf6/cyTd3KZXkZWK0JZGXO6fJ9xfGhwm3W7I3IdpGb4kRRzH8vSjhXkBp+VBQFhfBrFYZluRibl9Jv5ZaApQciAUt/jj8XQvSeYRg899PbqNy5PWa5xWrl2t/8iYod27A5E3C6ksgZPoLGZm90m9LSzu9AL/ru1/H98V/s2PU5wdQMFMOI5mhqu/dsu7kJKSqW5kZcjWVc0/Afcr76Xf5bbmdS9cso6OiqHSWtLUdKUYaCW9nDZf/8CdsjQcoB3edmJmr8a2pPR/qMBE7u4bbieGCEo9rvnTWSr04Z1M+l6TkJWHrA6M9eRkKIPtv66cfRYCUhOYWArxUtGEQLhXjqtptjttUSkmgZWhx9XVdXxy8eeYY5s2eSkexCCd9xtrjfofSzl3ACzqrwxHGKAcbBq8odBGl68Q/c97Pfk/DyUkaHzLIFm2G9w8E7rkSeNRJxl1lAhQTdYI49m9mFpzBr/NWs9Ozlx0t/jN2RepB3EqJrbd0c4qufgwQsPdDsM1NK9teET0KI3vPU1/Hmnx+Mvv7OY/9CCwVZ89ZrrH3rVbyNDaTnF+BuakZraUb1tXQ8SPVO3v9vW16W/IKtjBy5EmvCKEKt7U6fPQhWIjSLyvc+vpLGoiBWo4hQFxeNUxOLOHvcNZw96mKS7ElomkZz3QYAHBZHp/sI0RN6uOIu3mYYl4ClB9ytQQBSJGARIm58+PTfYl4rioLN4eTESy5n+gWX0NLURFJGJnNufYJ5La+iGDpJ29aAYdBqc+EecwoQwB6MNBMZNHvTARj9tT3Ub08lIcPPoAk/ZeSQE1BVK4pqMWdZVi1sq2ykft+lHcpVqUNjrROgy2AFYFlLKctW3c/PV93fYV2oSVIsiL6L1LDEV7giActB6bpBs9+sYUl2ytclRLwYOW0mJRu/xNdsps5vrqslJdscIWOx2kjOzALghXuu5Vc/r8BZt5ecQA0KkBjwkLjhbeqTB3H69f/L2bMnR+9GDUOnwVPH+9ZLSHXWsbf2d9gy/8CUESfFvP+klEJ+v+ECZma/CoA7kEqrlgFkkqhupuWALnEWxYJhGDgCLhRUNCWI39ax1kcxFArdkqlW9F2kk0OcVbBIwHIw8fYLFeJ4E/T7qC8vQw+F0EJBDF1H13ScySmc991bePmBXwKgaaFO989OSeBPD90NgK7r1Dc1s+gfz+Nf/joZzfvZ8OefsfLRRL7/yN9JSU1GUVQykrM5eea/+GLllWQ6a6jZcx1PbjuPs6b/kKE55hxADquFO674A17fvThtzpgh0lfrQVbvWUT1nl/iUnVaa1S0z2Zj3ZaNeTkxUAzQlRAh1YdiKGTlJ9NQ5kVXHSRfOP2Ifqfi2GbE6YS+hxSwLFy4kDvvvJMFCxbw0EMPAbB48WIee+wxVq9eTV1dHWvXrmXy5Mk9PuaiRYu46qqruPjii3n55ZcPpXiHhaIo0Wm1/f046ZMQoqMWdxOP3nhNj7Y1epDwSlVVstJT+d6t32Hl8pm89egfcbXW4Qy18MRNVzHrh79i9qwpABRmDeeUWa/y9mc/YlDC5wxxvsmODW+x1HcCOblf44xJX8NpT8DldHV4H5tqY9aIa9ntGEr54hsoeMQKLO++cHvaEsM1r7DAzbN79LmFOFDkTyHO4pUDB8v13MqVK3n88ceZNGlSzHKv18vs2bO5//6O7a4Hs2/fPn70ox9x6qmn9rVYR0Tkd6r3Y4Y/IURH7uqq6POk9AzS8wvIHDSY7MFDyRk2grwRo8gfXcykueeRnl/Qq2PPOHEqP3/qnzHLvvjDz3j2hdeid6i56Xl8c94zuPL/QnnrOFTFoDBhHTb3z1ny4Yk8884CNuxZ2enxS37wXfxzbyLzkd73jdMSO+kgLEQPRf79Hhedbj0eD9dccw1PPPEE94bTVEdce+21AOzdu7dXx9Q0jWuuuYZ77rmHZcuW0djY2JeiHXb+kBatWUlxSqdbIQaSpHA/FIBrH/gziSlHfrhv5eLHWDNqGNOmTogumzX2PGaNPY9t+zeycsuzJIaWkOpoxMXrVO95nUVbBmNPvoDTTvgGWSm5AHiXfNSr9w1efim7Vn9BayhIfrv3FqK39HbJBeNJn2pY5s+fz7x585g7d+5hK8gvf/lLsrOz+fa3v92j7f1+P263O+bnSIgMaQZIkk63QgwoSekZ2BMSAfCFZ1U+3AJjYpteWmxJFBbmdbrtmEET+MbZC7nw7C8g4/eUtJxEULeS7SwhNfgIq1ecyjNvXsWy9f/t8v10BdYMyWXN6MFot3yP7EXPMnrDl0z61b1kX3cdFenJGBaZ10z0XbTTbb+Wovd6fQVetGgRa9asYeXKzqs5++LTTz/l73//O+vWrevxPgsXLuSee+45bGXoSiRgcdkt0dkqhRADh9VuJ9DaghY8MpP63bjgO/x9/hdYDQ1v+mBu+/2DuBITu93HZrUxZ/LFMPliKhur+HjdcwQ9r5GfuI985woCtSuougeS37LgH23gH6ODAonubLTyOVSuWweKQuWHb+F87w3y0zIZOnka7qCZUl2VgEUcgkiTkBpno+N7FbCUlpayYMEClixZgtPpPCwFaG5u5hvf+AZPPPEEWVlZB98h7I477uDWW2+Nvna73RQVFR2WMrUXCDcHOWSmZiEGJIvNbKrVgsE+H+P9V/5Lyd49WKxWrFYbNpsVq82GzWbDZnPgmjAD7+7NOFtqaGx0HzRgaS8vLZcrzvghhnELq3etYsOO50hXPiA520PjdRrWSkh53UJL0dVM+MEPcGVlkPfqKyz/zzNU+lrwWVT2NDewZ9l70WMGamv7/FmFiCRvV+KsjqVXAcvq1auprq5m2rRp0WWaprF06VIefvhh/H5/h9lND2bXrl3s3buXCy+8MLpMD6fhs1qtbNu2jREjRnTYz+Fw4HAcvWyP8fVrFeL4YQ0HLKE+1rC8seg5Vm7dfvANB48G4MWXXmXB92/q9fsoisL0kTOYPnIGXl8ry95+FMubL5PxSQ1WHRI/f4FdL75I5bjpFFxxKVc++RzB1hZ2v/Eauz/7hLLK/bjDd8TO2rpev78QEdHEcXF2YetVwDJnzhw2bNgQs+yGG26guLiY22+/vdfBCkBxcXGHY/70pz+lubmZP/7xj0ek1kQIceyw2MxpkbVg53lWDmbHjh0AqFqQnJRkNE1D03Q0XUfXdTRDJ6RDQDXPb+XNrYdcZpczgfMuuRUuuZV9u8tZ+Y9/k/ze6wxuKKNo43LYuJwV992L5/SzmXTjdRRf9Q0Att94IzVrVjHq9HmHXAZx/DLitNNtrwKW5ORkJkyI7Z3ucrnIzMyMLq+vr6ekpITy8nIAtm3bBkBeXh55eWZHteuuu47CwkIWLlyI0+nscMy0tDSADsuFEOJAkRoWLdS3JiGnww4tfobk5fLN+T/odJu7/vQ0tvrdAOhBH7/+24uoFgv/+7WzyUhN6lvBw4YML2DIr25Bu2cBX7z7Bfuef5Gha5eR3uom5e2XqH37JdYXjcF15ZUMSkklw+vD0k2TvBEI4Fm6FK25Yydk18wZ2AoLe1W+YGUlrWvWxEwCa83KJnHmjLgbFitM8Zqg47APe3n11Ve54YYboq+vvPJKAO6++25+8YtfAFBSUoIaJ719ND0+x6sLcbyw2GxYnCGaGnZRW5GIYegYuoZhaOiGhqHrYGgYuoFhhEhwZZOZPz66f3JSEpUtfpo93i7f44SxI9n8qRmwuIxWgvs3AvD7J+u594c9G9l40M+hKsw+9yRmn3sSDU1elj7zKsFXX2bsvg0UlG6D395Dc2RjBfTWVvNW2TDMO2ZDp+nlV6j9y1/QukgLYRs8mJFL3ulxmQL7y9jVxWjQxJkzybn1h9hHjsKS1DE5nhi4IuNHdCO+QpdDDlg++uijmNfXX389119/fa/2OdBTTz11SGU6nLyB8Cghh3S6FWIgyj79JbJPh3ruon5Lz/bJ2XcbE2fdDEB6RiZU19Hi83W5/dfPPpkXLVbWfvw2NqUt47Xe2sQ9jz5Hq8fNGafO5rxZEw/ps0RY8DN0nI2QL8Doqgoad7mo3+ZCD5k3etW/eYDq3zzQ6+Oq3fT7C9XWUv/UU7Ru3ASGgep04vn44+h6R3ExltRUDC2Eb8NGWlasYO+VVwGQ9f3vkT1/fq/LI/qHNRyxaHGWDFUSixyEJzzxYZJDviohBjpDB8NQwAg/AoRfg4LFYf49V7f8jiWLXweg1dfEqFFJVJUO6/bYl501kwtOmcID/1iMXmFGRvaAG6PKjRP48O3XSU5JYU9ZNUFfCyeMGcbU0YM7PZbm89BYuZfG6lK8deUEGsvR3FWoLVWkeXYxPLSbExTzYqInKNSfkEv1CUPIe3UjIc/Bb54y//d/saSmUv2AGdQodjvWrCwyvvUt8yvRNAL7SrCkp2FNN2egLr15Pr716zs93tB/v0BCu6zmgb17qfzlL/F+9jkALZ9/ARKwxA01HLCEJGA5tnj9kRoW+aqEGIgyMk6lvn4ZCQmDOfmkD7vddunrNxJM/AAAS5rZvy7cZY7MrBJ0Xe+2udppt/Hz/72CzfuqeObltwk2VmIoKkm6lwT8vPvvf0S33bf6QxY5MjixAIpaNpDm3k6ubw92w4fDCJCpGGR29UYKlCm5lKdOJf3M7zPmhNmMAcq3n0nTqkqyLpxG5i8eM4d5tPtRFAXFbnZC3nvNN6KHy/zfmwjs3Yf77bdoeuklfJs3o7e0YElLI/euu7CkpcYEK2lXXIFiUcFiJfWii0iYGNuf0D50KIOffBLPp59S+u3/QWtq7PZ7FwNLZNLDeJtuRq7CBxEJWJIlYBFiQMrPv5T6+mW0tpawYeP3mTD+IRSlrRbi00/fp7r6UwoH5ePIHoO/fjeG4iOSrECx1KFaAthsAXbvWcbIEacf9D3HDcnlvgXfBMAfCPKLP/4Ni6cGVTEIWhLQVCuJQTeJ/nr27mnmYl7CitZ2AAWajQTqlXSarRm0OrIIJWZDch6OrGEUnXAmhYXD6dA9VjObo1SnA9XVfb+R9v1Yav/8cJfblP/f/3VYnjBpImmXXnrQ78GWa04zEKyuOei2YuCIjA6Kr3BFApaDima6lYBFiAEpMWFo9Hl19Zu0DFuAyzUyuqy8/B4yMstoDvdYVbtJ37Rz5xM9Cljac9ht/Oi6q9mweitJyYkUDM4hb1A2d/7qfpwEaCYZFY1l43+JmjuetIxssjLSycwdxBBLW21OMOBnx5oPaa2vYPenL7LTW4fS2oDVV48t0EhCqAlHfR3gwKvRde1M2KA//RH3O+9Q9/gTGD4fSWeeSdLpp6M4HSSMH48lK4vah/9Cy+rVoCqoDieOkSNJnDmTlK+c16PPrljD50VdZrKPJ8rx2un2WOcLmndFiXbpdCvEQJSSMpFZJ77LF8vPBsA44L5RUdsupj7fILNPi6Jg1rBERv+FgASGD/t6r97bMAw+fH05S1e9A0rb+yq6Badqnjtms5LPJ/6KzIJRGLof3GXUNZdTV7LZbMJBwVtXStaq3zNO39/t+5UZaeajlkbnvWPaOEaMIPvmm8m++Wb0lhbUTrLz5v3sp736vAfS/WayPsPno/wnd7RdCRUl/PUq2AsLybzxxrbgRvS7yK8pzuIVCVgOJqCZv1GbJT6GYQtxPHK5hmO1phEKNXZYl501m5D2Ij7faOad/9YhvY85NNpg55Z9fPLeKirr9hOwNJkXZ0PFhpMgrRjhYCWLOqaykf07/IzbcPD515pwUW4bis+WRtCRhubMRElMR03Kwp6UReXKFxjCdpqTh/aq3J0FK4eD6jKPawSDNL38cpfbJU6fTuKMGUekDKL3Ik1CUsNyjAmG24wlYBEiPigHTEI/bNjF7Nj5IjbbLu79+a9AsdNWDxPOYxJ5TvsamrZHI5qGKTIJS/gxXPFqV1xcftnljJowlAcXfoatrA5Ft/D19DtJsXnI9pdEy7NPHYRq6OGyGuF3VNmfdxbjr/glY9O6bux55eFwwDVA8ljZBw1i0COPENizO5wPxjC/ovCFsOYPfwDAv2ePBCwDSLQPS3zFKxKwHEwoGrBI4jghBrbOz76DBs1iw4YknAkeUrL2UV8/KHaDvvxpGwrpzjwmnDCOSTPGkp3dNnHrrbfPYuFPPyGtPsTy5m8wL/3XFFAFwI6LX2XUlM77yPRkEpLR21YBkL78I6Dnw4hra0vZue4jFN3AQMeIdN41wh9fN8JJ6ABDRw8GGTX7fDLzux/qDZB81pnAmZ2u01tbqPvrY1QtvJ+ECRNwjhvX4zKLI0f6sByjgtIkJERcU1UVvWkGJHxIUbrB6bOuRFVVVDU8DDj8PPJaVVWU8OuunicmJeJKSujy/S6+diwf/HE9JYGpLK7/NYPsG3FZqqioTWd4SMdiPbTzidKLC01TXQV7zj6X1NbeXZyqeZhNd16H1ZGIIzmNiXMux+7oXdNS9ve+h2/DRryffkrpzfMZ9p9/Y83O7tUxxOEnNSzHqEiTkFVqWIQY0BTDPJ1t+fxuEoxhEGluMQxSM3cQBFLSNqN+4jVrGsInaz38Y1bQRJp6FAouGkn68LQ+lWXC2Gy2fmUQZW/tpypYTFWw2FzxUi0rXvkQ+1AXequGM8XOuEnZzD19cI+mK6nOLiKnphT3zFN7XJZVLz1KQThYqc+wYSiK2cSlgIGCoRighBunFMir9Ef3zb7v6ejzJee8wgV/Wtzj9wVzFFHhH37P3iuuJLBnD6Xf+x5Dnn6624y74siTGpZjlPRhESI+qF4nOKFJ/ZwmPu98o8Qa0mrc0eCmO+XPbiH9Fyf1uTyXXTyGPZNz+fSjUspKmwk2Bkj1aKSHFNjZYm5U5mfHlmbWfFzGrXeeiN3efbk8rlRyakrRk1N6XA7/R8sA2HZiPpf884ODbh8M+Hjntquw7K/CEghhr2smtxHUir7lWrGkpFD06CPsueJKfF+up+JnP6PgN7+R+dn6kdSwHKNC0SYh+eMSYiDL3/VtGpM+x5Jiw+Iys72at5IqGBoBfxClYRTevFQMNTzsFiXah8VQFBRAbQ3iqmklpTVI484G0kam97lMw4akMeybadHXH3xaytoP92O1KlgSrXjqfLiq/CRX+vntT5Zxw09mUJDT9ezPIaMBgNSnfsunz/2ubUW7WQgiLwwFLCGdYW7zpmvQVdf3qMw2u5ML/vzftjL/9Wfw0Ivkb63lo9NOiNnWUNo/jwxpDj8YbX2TFd0gp8kcAu1+9TV2FKicccv9PSqPOPyO28kPj3UBqWERIi44KoaTqw8n95ap2PIObfbgTXd/Rqpfo/yNPaQt6HvAcqCzZhdx1uzY7rXP/2cL9e9XkNZi8K9fLOf/Hj6zy+ahfRleRu+DpFYDWrVOt+nM3nEZfOW86/pU5owRZkdZZxCc1YE+HeNAG794DVflpczIk5FD/UGRTLfHpkgNi1UCFiEGtnB+uGBN6yEHLInT8+DTMpzlXkL+ENYjmOn6qsvHcv+2BpL3+0jSFV5/Zw8XfWWEOUQYYppO/jHXwTtTLVxccC2zhxSbo7IxMCJzwoSHS5vLwiOBVAunT5/T5/JNPvsqdr5QQFNFSbvOvkb0wRzKbIR7DOltw5vVcEcZVUFRVFRFRVEtvLbvDV5R1/PS0h/znwv/Q1ZCVhfvLI6UyL8oqWE5xvhD5l3Mz17eyKc7atvmGQtXJZvJHJVIUseY14S3a5f0se11u6rbTtfTdqLquLyH7xHeufNjx54IO10ffk1Xn1GBRLuV8ybkyWzWol8Z7SZxa/6gBP+uxvCKtgtrzCNEAwIA/65daA2N0W1sKKAUYlfgvz95m4ZEFUOHUIMTgIT8djUNSrvjtnuuKIo5Utgw6OxetkVRqbQ50G0Oxtqfo2joahKDIVKXt7B0VQaqP4ASgKDiwszYonC/4QeSUWpfo2rjWxhETybhd1Ci22LTIMEg5LSxf93jMU1gZsdbBQ0L9aQSUhIxVNXMYaOoZifcdpMqGpHAAyW8HgxFBRTsuo9cXy1JFgNUC0Y4MDFUFQ0oVzQCSdlYrA5sVgdjBp3AsF27Sa1p4W9P38qV464xR2BZraiqxRy1ZbWhKCqKxWqO6LJaUS0WFNV8VC0qisWGarFisaioNhuqzQ42O8F9JdiGDpU+Mt1oSxzXzwXpJbnKHIS93fDDtzdV9mNJBq6SOi+3njOmv4shjmftzrzBCi/BCm8vD5AU/qFDXpZMI4nquti5clor7L0v4wFUoAAd8DJ4yGrO8G+OrivW9ptn50M9Q/vCPwPMlZEnFVuh4pXDeuzIb0YzFHRUDBT06I9qPhpq23MUjJjlbdsZkecG6IqCYVXRFfOYRvjYhhI+RjiwM1DbnivmMVt9KgYqVrsjvFwNB5uW8LbmPgE9PHLLAIvVitViI6ArVDYHsNkdJDgdKCjo4aDSabOQlZxgBmeKagbJ4cfoMtWC3ZmEak9EtzpBURlSW8VURQU9vvLiSMByEPdeMpEzx+QQ1NtVe4bvmqLPabtbMyJVtLEJH6PLCG/bfn377WPeg47b0en7xW4ffY8uyxN+3U1Z27/PgZ8zMvpzS6Wb3TVe3OEJIoXoL4pVJevbEwjsc3exQbsOoTEBiYLW2Ej9k0+CquI6ZXZ0fV1DE9uCiey1NVM0ooDkrHTKtjVhc6gkZzrD+7cFSrE3q+3mFYpcPKIFMFAUBa9u8O+QDQCnb2hMwBKxjUK2J2ej+BVwu/CioxkBXLaE6MdQwjU4CoT/iA2segAjTQMdNNUOhhGteyGSLM4waCQN1TAv1zZC4ePpZufjcBubim52oI1c1g2wG0Gcmj9ahsglXFF0LIb5oyoGhqJjUcywwYqOBQ0LBlZDC7/WUZSYyz9qNMwwl6noWJTYb7cnLIqBhS76+fS18qXzyrIeFCb82PNuR9D+tGrFbPJs6WS7ht4XZwqAAz6qSQMOnhxwoJCA5SCykx1cOfNg04wdn+57cwuP1+yOqYUSor84R6XjHNX7DrKBvRqVO95GTU4m7//aRt7kAZ/e/hPciU6sTVWc9/27DmNp4fH1W1lR04Kuqoyz/pBN729kPG0p/FsNOzsHjceWvB0lxY/VHqRg33DGX/osjrS8bo+9+plzaSzYR3rZaKZe23H+JF3XKdm/mlm7zIApT/Xx8NBBzBqUj9XScaLX7ZXN+EMaFlXFqip4vnyeUS/+lIbtLlrr7Oihzs8BCrDo6w7u/sUalHYdid+fO5WC/a00/fp7zLq064y9hmGgGxA0DDRNw9A0dC2EHgqZj7puPg8GMUIBqte/xm7ln+a+AQUsmDUblVZyEuaSNuw0DN08jr+pidr334OSfShayAzmlPDIJ0VBUYnmq9GyHajNPpSggqFCaIQN2+BBKIqKoYA1LcuMifVIH55wdh9dZ1e9H+r87epxgEiAFw401XaBW2SZGTi2BXI6Co1GQvQYkcfBGU6cVhXC22Po4cBVN4MrPQShVuy6D7sRAMOgUNtPstHMlLTe1kT2LwlYRJ8FQjJtgTgGhC/QRjDYYdXMGdN5eeNG1gcCJDz4IGcvWIDFZuuwna7rVFSsw+FIIStr5EHf8vaPl/NP3QGqykhvE7edOYNPm29i/Iq22ZMTlADzypYA4DdsvJc6lobJu1n59rnMvPAD7K6u5xwKGeaFKMHZMeF/INDK+x98DcW+G5QXAKjUnVy2u5ZZ20t49syZuNrNrHzHu9v4h7U15hhXrKniO8u6fn+/PZXNY79JyJqAvTHEfXc/y7AT07j6ggvNDSIditWOwVF7iqJgUcCCEh6p2fG7b0+rzKDK58Fab2fayS+x46OfUJ+5CYqhmRcIlr9FftJV7PvnCjK2b6egk995p7ZB1tOPU/Po3SifV8BGgEqzP49hoE1OZcKiznP/NJeu52erqkht8XBOgoWm6ipCPh+qxYqamGD22wn4yR5UxHfOPgun3cZfP/6Myg+XxBynVU3gy5TYvEBDMhP581VTe3/T+O9vwuaXSU2IrwR+ErCIPosM+bZ3ckcmRLywZmQAYPh86F4vqqtthNGkr32NbRs2sEVVWBuqQHlvMopSTMgIoqrJGLoP3ajGZq3BYg1iGAqBQAqgYBg2kpLOZtTIr5GRMRKnMxmAx9dsNIMV4OxgM4+fcxIJdjsXnf99/utrZsr6f5BtNBHEShALKgaZSjPz3Ot5vWkI5HhY/saZZNtOx+HKw+7KxpacgyM5BwUbQW8tRouZrba13aSLAKFQiBf+fSUFBdvRdZXr9Sd5w3IhNaqZLv8LayLFH63j+4nw45OnA7DH549274motrcFK/Y0hdQzp5F592MYoSBoIZ65/TmaMfu1pQaAGmh4HX63/VksNoUpXgfgMy/4h5GumZ2hFV0hqXAcU655Fc/+Tez4+Hbqs7fgy3Ozh8dwjVWxb7LiTUtDPf8ruAYVgdVCfVUDW3fvQNOCpLS0MsTrIWXLFgBqr7uJpqlTSFYqqExNoviM30ff19D8lNz1Loaih5PPmEloDMWgGHiCVF4vSOaptACOvETy9SAPz57KqNzc6DH2Nzbxr89XsHnjBlJqKju0WhWmWfjND047TN9UnPW2DZOARfRZpIZFmoREXGvXVHHgKE9VVbninnv49Im/0VjwJ6wOH7COrkJ0RTFwOJqir0Oh59iy9TkAdN0cWfeichcoUwAYmlWLqrZdmr76tTvha3fGHHNn6SYy/34yAIUJ36ah9U8EsryU8aa5gT/8U9tup1HmQ+CADg6NjXXk5Zl9ZRITv8X9J9/B/cCO2jrmr9zEdosTn83O7/2Qu34L35w0Fr2TCtSGlJF8cMZf2i0Abvmi7bUxFhTIGLsX6+hCql8J99XZnm++d9AFNIH78N7hG5pZY6IYbb/TpEHjmXLN63hKN7B8xyUAaIN0Bp9RS1ORE33WcNTEWazftIEvvLsht22Yda01nWDRIM5Z8i5up53ddeXUjh9KyGKhuN37KhYHShf9U6xALgaX7g/x6GgzQ3E1cMenq3nxa+cDsGp/Of998u/YdI3Udvs25xTgqqlANQz2tWh8se5tclPHkJCQQG5u7iGMhIpkGoyvwEUCFtFnErCIY4HW3Gw+UVVUV+eT+82+8X/YtjLA/ua2Pi6KcjEW1UlS0lCys08gL28idXU7cbtLAaio/IQW7wdYbY1YLCFU1bw4nMl77DJG4lWSeaJpGDP2r+SioSd3Wb4heaOiz+uTc5iR+xz7Vv6BllAJIaUF3RJAs4XQHRrYQPGrWPw2rMEEioZ8K+ZYqmojFLJjt/vw+f5GdfVF5OSMZ1RWJku+chq+QIDLPlzBKnsSv6p0s2T/Kpa6Ol4m6lKS6G74kaEo+BLL2R+0svzT3exLz2Boa1q0z/NZ3nIAgk+/xDOrVrE38CkjMkaRnpAKzgDWEVtJtDvJCExAUVTSCk8iZ/oVeP/zF0Jl+0C1oFhUUC1gMYdCo6r43OthMrEpeMOSiiaSuzGbKkcNg1QvjjyNguBuWDaf/eTRxHQURmCgmhUQClSHGiAjg/fnnIVRW0qAttFin1Qv5pScrwEQMHxYz0ogfVwOhh7uwxLOg9PwZTnW5U7s/haeyc3iG1Xm9/ZJegGzXn6XsapOY0hnlMWKTY+NelJy8/AOyyBx+UaSfa28/fIXgBkYXnXVVYwZ08fRmZZw05rewyaxAUICFtFnkRw1DglYRBwz/GbzieJwdHvHOmbGzRgvbaAsfQkpZUXMuPb3HbbJz59Efv4kc/sx8wCzf4vXW43P50ZRVFpr1pNY6cOL2UQ02NV1R+Gq+nL2PfpVZmL2YxkyeBIpReOZOHJRnz5rRkYGQ4b8jIoKswPxl+svJhiYwfDh32D06K/gtNu5a9xwvr6tHI/Nwfvh61qRR+fJScMxFDBUhYQTLNRbS6naXImhG+g6VFYZkbFKaNYWPCk7wQMTrFCounnFmNChPJn7d5C5fwfTAFgPQNNXQ3in6bQCZYlm8Lff8waF9z5DU9N2bJUKzjUqSidBSesJOkwGNdh5Rt6AYQ6zsU++ntZ5N1D3nzsobPiYQUolV/A6LUYujP8Ozgv+l22rdrBry078fh8jTzqfxrI9rHrnXwAUZo/hyod/R8Vnm2l9uQy76kT/IETJlu2M+c45WJ3m4GpNC7Ht4XUU2AfTGKohpcbKI83N/LnBx5bBY9ibms1eQNUaWTW1mFN3lzK6pj5aXv/OzeSNLaSzsW8/eu9HlC43vx8FBR2dc4acw23TbwuP5jTQDZ00RxpJ9gPa9NTwL1aTgEUcJ6SGRRwLtEazCcdobcXQ9ZjRLAcyjMgdds+r4g1Dozzo5Yv6XbxXW8+7gfHRdRMs+5icfXGn+zW4a2n409nMZD9+w8arI6/n8qLxnW7bG+PGXonDnsOWrQtJSNiNw7mCsvIV7NmTglY1iQzr5TwzbhgLN2yiPC2L0VWlXGyksqZ0J5mZmYybMorhgwbTcs5wdg220OJtJS0rhfR91ax+fzsJLYUYSmyqg3S1ldPSLCTakzEM2DV4EiNK1lM5fDzrEyqwWBtQDBiSVIQ/cx9p4ZqM5LICmgvN2piyU7ZGj5c5LEjGmkzQ9fCAGDO7rpZq1pZZEzv/HYaCTeC0ou3fQvLMMST94CUCe/bg++Av2MsWkahUweZ70Db/iWFDr2XIBf+DlpBE/ca9bFu/Jnocq8UMSPJPHod7UCb7/vYZ6YEsUitT2fWLd0m/fDQ500bx+h//xTT7SHR0Pknaj/vDHQCcDoyvKacyJYMGh5tq4wlQDPTAeGjX2GRv9eFeswsAr9XL7uTdAAQsAUoSS9CNthofgLf3vs3be9+OWea0OHn5kpcpTCpsWyg1LOJ44w8HLFLDIuJZqHxv9Pn2yeNIKHThHDMM57iJGJoGofBJXVForVwJZ4Enp4yGrf8ws7EqFlAsKIpKVdDH/fUqK4NFZCjmxXO/noaHZCA//AMjlBJuH5LMOUXndVmulX+9hnPYD8DrE3/A5Zf9vNPtHvji55RXLGZY8iAKkgehoKKEs9Ca5Qv/hLPYmustWAefSo7lW5TvfxlFWYfd4YbBn7CnsoIdr57MrHbvsSv8uKcaVm1ZhqrbcCoKGXoy4cxNAGSrOQQSa2hMMDv7KtjQjCCqAsN9X9AaSkFXrXx05ml84Tqfy+fN4SSbj29/+FUMQ+G1i/7J3ur/oJc/RI3uYs61y/jyua/RaN8MhkEo0wyE7KcMYvC9yzp8F+uWfQeC7+JJD7B8+wq8rTWUVS0FxcHYYfNoTjYveUl7P2H5vx9gxqW34RgxHEb8Dr355/heexTb9r9hoYY3P1nC9rfWxhxfQWFo3iTm/ugH0WUpg3MZ/4uL2fXSMpQVPlyWFFr/vZ+9lQ20hOd7UlFJwEGL1hzdL7epjtymOvamVFCdY36DzWo1LZYirIY1/G6YObEUgxzLl9zuXtnWXfasv2MMPQWAlmAL1751LZ6AJ5yVXEFVVHyaD5/mo9xTfkDAEk6vFzo8c0MdLRKwiD4LSMAijgHOgrTocz2g4N3TgnfPJnh7U4dtQ+eZFyDdprGm/N7o8vc5GzcpvKhcHV1WbbQd10KI4WoNo+xuzk23cOnoS7Cq3Z9+h7WYF/2VtjFc2kWwAtBQ+QIXpoaAHeDd0e0xD7TKOpEbznmZlpZG1n74R3wJT5Obu5uyfRNp8Sd3vaMS4qv+U0ggNuNvqzXAs862QMKOk0nnXsiHH31MUqCOhFC7xg1/Df967gUe/Nlt2EJFBK2lPLH6Dc4Oj8RWwpfmE65eHN1l53sXsU/dZAZfnVhXW05RuNeqZ/9VABSEv+amkmej21lDBidu/jVs/jVBw+xCvdc2gsbMyUzATQLQqnUcQj153pWcdd01HZarqsqoy0/HfXIV+x75lHQtG22pl5NOm0j5qmoKWtM4Le0Ext96Zod9f/vmb1lRswJ7UMXpqabZ/SYXrtAZVhW7nW+4n6yZ4RqVIafA8DngND9so6+R70/5PiE9hFW1YlEsWFQL9y2/j9ZQK5kJBwxBt4Y7O4dih6sPdBKwiD5rq2GRYc0iftly0hl7ZTmGLRXf7D/i++IDWjdswrevGtVhRW03T5a1tpWWZg27qrY7exo86fxOzDHHqSVMT7azzptEmd+OJWhF1/NotBfxmWan2l/OuHQXkzKTyEq0d9p3Zp+zgFG+cmYEt/HZ/aeQeP5PmTypY42My2oDQpQHFBTnMCCSOKzdj6Fj1oTo1LXUMjHRDLyaPebQosTENGbPu5sVr2+mOXEVcya6yci5iZIdeymrLKfe20iz5sUfbupxYu8QrDRZWzt8jsF5g5h38iTmnTyJ5Vv3sW13KYahU15egb9sCzbNvGBOTD+FNc3Ps6z8Q84pOtH8VsNVCfqKx3B7t2CkD6U2sBWc4LBnd/q7bGwYR1GqGWg2+ZMJ6jb2e/LJdDaQbPdgM6C5aRD/VSZyqfE3EhU/tvDwnlGh7VC1PXqsCwq3srExlx3NWVT6zOBt7RvP8/Hylcy6+FLOnTu7w6zaKYW5jP35BWz91RukhbJgWYCEVCu0gqF1PiJnWPYwqIGATWf7IIXtgxQ2DFV49C8a1nYtPqtPGM+UX7zW6TH+uv6vPLvl2U7XAR0nmIx8uUp8nbslYBF9Ful0K31YRFwLj8xQHE4SzvgqCWd8le7y5Y7qbOGH62Je7vcNYrMe/ruwmD/VwC5CEAxBfQvU18MuUAIazpBBsqGSabFQ6LAxJNFB/qm/xPjwLs4OreVk3wYCL13NCxu/yxVX/zrmvUakFUNgLRbXRK4+9b/dftT9TdV85eU5zA0EuSAtCFpqzPrhIxfwZfm1NDo/YvSQ/2PYzHOj6wzDoKXBQ11pNTX7q6hf2UyGzxxVZclwMv7HpwLws8CZPPXbxykNVuNvrMXz+PnU5Z7ChJO/wYnFZhPG6q37eG3RFlRDJ6TpXDXhfNZ8/jwN+kaa/RNJBFxKK0HNz6clv0bLsEE91BnpvLH5HAy3lczVf0VRwGVT+P7l55NTUMS5zjReemIKSkICc39gJuEbEr44a5rGur+1VVs8MuYc5s7SyS0aSv7fzGHm9UYye076NVmjZ+EzbFg3vc1J6/9OYvNOvqgbzM7mTFJqd7L577/hixfyKD7nIi792lewt0smWLNmB4mBZCKVQOlNZofXulBdp7+TS6dfSm5KLvsb9tPY2sgjJY/QlKRw9Y9jg4mRSVb+p4vfa5XX/FxjM8ZSmFRIyAih6RohPcTM/Jmk2FNidwjnq4k2DcUJCVhEn0mnW3FMCLaiGw4CTYX4H/0XwXoDPWgFxUC1aliSDFynjsU+dSYAuzcux11XTjCkEQwGURpLGOcaweaktgy3bruZAdXh1RhvszPE5aA+pFEZCFKnaTSr4LcqYFEw7BZa7dAKVGOwhQC0BgAXzH6I0/Yv43c7H6JIqWX41iVc9+hg8lOsnDJmBBdMOx1VNec1MnR/lx/R7QuS7LBiVc2mgMi9fprRyEsf/gQUKyg2wEYagKqzccVvOenCJwDwlzRR84g5ikcFzHRnZrCiJFrJ/f6U6HtZ7FZOPHEmpZ+8zrd8d0I5JJV/irH2NzQpqQQsiWTas5lJBnuUwXhbfZwzchK3L8tGt9bwedl65jghUTV4/6Nx2DJsGIY5HdRbJSfz8f7ZHT7fur8uYf7JOZTvraXKkcMsZzknTjaH/Pq8LZRu3oO/JTYNfcI2ndf2womX29Cv+ZiGvV8yZPp5TEtvS+bGiJvhopsxtCCXWGxs3rqbl595DsvOVaR4Kilf/Dj3v/4CubPP5cqrL6Xipc9I2ubEHv6e6501bHDs5fSmGVRrVVz4jwu5svhKrp51NYqiULW3iRfvXx3+HlPRScY5zoXP5m2b/yrM19R1B9nWcNPOzLyZnFRwErqhk2BNYErOFCydZROWgEUcb6IBi0UCFhG/9NLNVPr/jk4a7Otkg2bwLy4lb+pMvvzgBU5YelOHTUaOvZvNSSOxhEJk+RROSnZxzfAcThmc3uVQacMwKG/x82Wtl61NXnY1+yj1BagOhGgwdFpUCFoVlg46lc3bX6bIUssgpZal+woBhRc2ePnHp88wb0ICWQoYRtcdKGc8ejP2jLbEbq3hpobCpEow/tNp4tOWhI/ZtaWKIYXp1Dy6vsP693OtrMywYMtMwLJ0GypmKv00i4VL0s2am5VMYkZ4uLICpBpNEGoiO1TB0PBx9jz4AWsHXcDIxBlsD77JqrogE/NcZKhe/MFE7l5xK1UtOViszWghs2nGpgb5cbGbP2x20YKTDYE8vvMRwJmQBzubt+K47y/MuPBcXv7tfYT81aAk4Ez7LgBpJ+dQ/XkVGX6Fbc9sZ0Whk9t+fDUJjs5T/yvhUTXjiocz7t6fUlZRzaJ/LsK3fimuQBOeD//Nh6u2MT0rtskutTWd031m81ViyMFedS/3b7+fRTsX8btzfkfr/rZLsNHiRAGuWvsz3M5aDHR0RWdn1mo25i8jsTWt07IBBHTzd//Pzf/kn5v/GV1+27TbuH7C9R13iAxntnQ/1cFAIwGL6LNoan6pYRFxLGgbjR4eRpuYvRutRcXvHXrAVgbNTz3P4F0f4TEuINHyEaW2LHTFiseaztXeas53upk77WSS7D07rSqKQqHLSaHLyfl0Pi+PrutUtQb47weDgFXkK/W8av8pjypf5b3gJNZUZLCx6nyGp43F6SwnJXs9F42fFHMMX1BDdVbELFvlcVGknEa2w45CAIVQ+CeISogcdwsj9p+B473tVLbf0aaSeXUxuqbz06r9BFUFcwri2IRnDnci86/7Xxr3zYWPzXw0nm8ux1vyOa17V6H66glU72BIaDfDlP0M2/9Xtg/6LtttUBHawwWnLSfRZuW2l96kqsX8PiPBCkBQtzH85Ok8O66JB97cQMhQMAxY5SsAwGtJZN+Xb7Dvy7aJH1VLWz+OxBo/Q8ZmsKeuBWeVn/QyP4tf2s41V/ds2Hhhfg63/eQHNDR+i0cffobA5qU0BWpxB+pIsWcS1P2E9CAJ1rb8J6ktNs5IOoNl7mXsZS9XvHkFX02+jAxmRcYDmd+dlkC2t20OqPJUsyO1w9p1VuCvj/46noAH3dBRFIWt9eYQ8N+t/h2JtkS+PubrsTtIDYs43rRNfigBizg6AhVe/Lsau97AoF2HwshCpe25YgYKilUFm4piUQjWFwD7sWQ6ybjtm+h11dQ//ha+puHRw4ZCuTRtBfgGAKWOmYz96YLD+dE6paoq+S4n19/5OE/8zsnVwf8ySd3Do/yeB7N+yJJQEdvr89haPwYYw22LtnPRrybRtO11fvThLXyRkIDVMBiRYGcfoGrwjSWDUQ0FQ9lNRV4xiuIAzIuhWRmkUBtK5ITEiR3K02CppubdSkJDsgimmLUol/gs5NmsGAYsC/jYnKjwAC38dq+X7/gd3B3ed8OXixh31vfIPf2G6PG8TfXY/jAMgNGVX3J1SjMbHH7+/N+buXz8OZxQMJGXV3dW7QXf/tse/vSV8dx7/TiGDUpGVVV+++Sn/GV7Y+dZcpSE6NPyHWbunRPnFvGxv5y0Rg1PQ9dNal157HdrSa0ZjZE8kqbgDt6ufA1Di/STUchKGU22ko7T4mJr4nb+fOmjrNq5ijuX3kmFpYL/eBeRN3kZV9tvJN9ZEP7na5j9pA0Dw4DVzebIquS0roOL84adx3nD2mp3Xtj6AvcuN0ex/WvzvzoGLHo4T85BRqoNNPFVWjGgRGpYZFizOFrqntqI1nRkckco4Tl91Mwcsu74JqGd22hZuhqtKRgZZENLrRnEVDonMvaIlKJziQkObvzpYyz54ruc8/bpADiDPt7+v+tZs3sjj3z6GR9sGUwwmMCXFfuo+uQ3fJFgXqBDisI+zCaAFK8NNZwhVjE0kis6Dt2OqC8oJsORF7Ms3ZcF5RCqAM4xl32ihlhcXMToQWm8uKqE7zWbmVoNReEJu8Ltih2nEeCkdX/gM4vCyRfeHT2eKzWDVUlnMt3zIWeEPuOMSJLXisWwdTFJgWKyWr/KTv90QKFJNXg3MUhZePjMD94yy+8wIN9moyScMycpYzRXzp9HXVllNNlfKKCz4SM/fp+KFtQxdINWdwDVZYVGjcaaFl5fsjvm80byoERfK7GtZwk1ASL5biz2Mai20eihUoK+lRDaR617W3SKp4AlDYDpI6fzxtA3uO/t+/hvzX+pTCjjT/q9nJt4LikJKRiKQaotlURrIsnOZJq2VoEOSY5E3N5G/vPJP/D4mzEMHd3QmVA4mXNmfA1foJWn33+Yem8tOgZDrQXsDZUT8HpZ9dritnIbBiPKS8kAdOhigPjAJAGL6LNgeJie1LCIo0XzmBck+9AULGmdV5G3v7s2ov+LLAjfvYZ08ydoPqIZuGblxxzHOnIMKSNj52pZ8dfVTNjbQtB2QKrzoyQrrd1oj7TBqKqF6SNP4NEhY5nwq5cIBlL42iPLGZr1VfyBCoyQCy2QjTVxF/NzAuSNslG+fie6u+1LSRk/lVCWGYgZRuQOX+fzYDMTPU5SUq2oNgOtIUBSUxJ+WgmFgjyy0sJtUxKotSt8Zcsefl+VxWXTB3O2L5/Hl++lKRBEM+y8MOZXzN7/N0Z6dmD4PR0+U9o5P2bl+w4U3c/05g9j1uXbt3KhfSHVweGs8FzF5qTBXKSrLDPsNGGnBYV6Q8evwN5QW6dUzTAoHDOYwjGDY4437Svm45P/t4zW5iDbV1ThcJnnr6TKAPsW7+3V78N6QF2Ooig0pw/lrgevZ+fKTSx7/gXqy8wMudZQTnQ7m9XG3RfczUV7L+L2D2+nQq3gjaY3oIku5Sfl85tX7+LVwNLY92z4L0tGnMgbKxbx5+qnO+wXqGvi49eejFmWVriXjBSoKSkh98RefeR+JQGL6BNNN9B086QnfVjEURMOksvqS6kPeTr2FW13/bA2JeLSU0m0JrRbr4R/Oh66dmkZLCvrsikJIL/OHI1h6Ed/ltuGRjeDnj8zWpbxU9omTHTY7Pz+ynH84JmdaEEXuyrMJqKIQOtQ/lAHzi1+RuVPYFBOOb5aDym+Rpw7Srjiq99g6sTRPS6LYRik3fEJ/13m5SfTE1mXrPK/7jpWv+vhZ2eO4f9Obz/4exyfP/clI7fvQA14qKjajaKoZGQUYLfaGTnpZJhkfhZ/cwXbP74Xf+M+MsbfSOOzi5mY+DY5tt1ckP5rZlGM/8Qfc92sc/jP5ioy7VYI6vyothrFE0T1hFB8GgGXs9vyq5a2fwB2r06jS0FpnyfFMGseknvZStSYqDDmVDPwHTljPCNn/JJ/PvMS7o81WrNDHbafMnQKb1z7Bg++9yArqlZgV8xmH6/hJWgEadVbaVQbSdaT+Z+T/4dbnr8BbJDWbCPPns1WRzmGCvVN1exrNJvOUv0Ohir5KOH/ZthHMeLUfPOfTbgDuLLbnLE72MWcSwPVIQUsCxcu5M4772TBggU89NBDACxevJjHHnuM1atXU1dXx9q1a5k8eXK3x1m8eDH33XcfO3fuJBgMMmrUKG677TauvfbaQymeOIKCWltGI5ul0xZjIQ4rXW/7N5ftTiXbndrN1u31vm/CwRge7eAbHW6qhWYjgRylEYCRr1/G0tcLUBRoUlKoK5zD36+Zwyvbd1LR1II3GCDBamGEotNU3srn7iyacLEhkMcG8mifbOaFZ3dwespqHllwCS5XQufv346iKBiGgUuDbycl8vvWVvYkKDxu9fPlO+t54uTR5KS3m/k63Cxz0vbnYPtzAOxPKCD9B8txJbTVGjmS85l4wV/a9ptyMb7qCmpeXEhO1fNkKVth+bf4YstEPh76LT5Nn2pul2zDSLYxotlgR7KCrS0DfgcVyyuZ4LKx2R2gWQeHCpefPYzhpw/CmtB2SWyta+XJuz4HoCg7HACFa6DczUHcgdh5fAZnO5n/q46zblvTbKiWdNSmZv7492cYOWoQJ046gaw08xdgs9q447w7uixvk7cJu9VOgiMBj24Ozb606GK+NecHzH7pNACuWHpddPsT7GP4y/VdJ5EDqPjlv0CvozdzYg0EfQ5YVq5cyeOPP86kSbE90r1eL7Nnz+byyy/nxhtv7NGxMjIyuOuuuyguLsZut/P6669zww03kJOTw7nnnnvwA4ijLpLlFqSGRRwdmqHxt5zFTPaOwTksDbst3CRktN0ZR541NXpI2mI2BxSNzWg7SLgjY3vKgcvCzUZtB2xbWbXHTciAXemHPhx0V+Mu1lavpdHfSIo9hROyT2B0+uguh0Gnp7hYPvcf1L53C9OU7RSpNRRR07ZB2TL4zy+YbiSwKOVbfHvBL7FY207xeijEvj3b2LZzB1tK69hW1cwadyZeHHgtLj52pzDnly/z6HUzmTJ+xEHL//gIO0+McgI+SGgr8/JEuPCLbSybOwm7zcwBklB8PnV73yZRa0XBwKkHGNRazrZ9XzKm+NRu38eZk0/BzX/CV/Vjal66j/Sa/zDLvYGX1v+QT9Km8MDQb7HZOZGTQ1YmpCbwe93T6WW4fE0VTWuqSd7ZSB5gSbDwmVcjoMO7L+9m8q4mZs8/Ibq9Fmw7x11wz6yYrLbv3Lsc9/7YvC52Z+dZYwcXFtCIm4RAMqxMZu9Knb3PrcWdXkXOBCcXnH8quelZne4LkOpqC8xrDbPN6N9lL/PGM+/AAbGlosPMQbM4GKWTZ/GgTwGLx+Phmmuu4YknnuDee++NWRepFdm7d2+Pj3fGGWfEvF6wYAH//Oc/+eSTTyRgGaBiali6md1WiMPFwOClzPd4KfM9Pvv6ZyTbu57rZv32rSxbV47f6uWc75zR7XHXvP0ZK197GS3ow9B1DEOPdtRULTZUixVD19C1EAGfhqI4MXK+0ufPUemt5Kef/JTllcs7rJucPZlfn/JrBqcM7mRPOO+0k+C05WzatY9lb/8bf6uHgA6Z3l18SzHTtqcordzU/Beefn4E11377ei+qtXKsFHjGTZqPAcm+P/3W59x94cVVKopfP2fG7h1+h6++/W53X6Ofw7vetRKlQ1qWjxomsbgjAwmT7sYprXNSr3ngQkMaylF0Xpe++XMHUT+zY9Qvv9HvPfmL7my4jVOaVzLKeu+z/LME2k+7XZWNg4BYi/DFfWt3Pzpdv78qZv2/2KcDpXCkEGZ3/xdr9tQx6bvftDWEthusNmBKfiD4dqVvFQ72QUuLFaViRcN67Tcp06ZTuYtu1mzditV+9xoVXZcrWmkNOTiWwbPf7YSdVwTV3/jHLJSMzo9BsCe8m3UOVsAaHaFaMasRkrx23njqiUEQwFsVjtpyZ0PkY8VCcKPg4Bl/vz5zJs3j7lz53YIWA6VYRh88MEHbNu2jd/85jddbuf3+/H72/6xu93uLrcVh1/bkGYFVY2vf/QiPoX0tj4AlsM0B8oLv/wT+zct6dU+BqC4N0GHy/7BBbUgN717E3ua9mBVrUzPnU5uYi61rbWsqlrFupp1zPvvPD6+4mMynF1fvMaPGML4+f8Xs2zXz4sZobblWwnuWwF8m574+ldOZuq4cm7860fsUVP5zRo/n+94nuvOHMf+6kZUVWH04FxmTW7rF6MbGl2NMfFZFKat2QPApPrl/OP0GRRkZlLqbaC6pYokwr+/1j09Kl97BYOGc91NT+Gt28f2d37NmB0vcmLdcvjv1/BnnUpi8V3UKE7+tqqEbJedu3aXU5vUsZytl4/gkikF7PmwhDdf2AlAMDL9Ujspjo77RmrkikalMfN/Jhy0zOOKhzOuuG2Y/J7yUj5atpqalSFcngzYkMNTP1/Gqd8axoknTMLtaQBFwW61Y7PasVis7Knc3umxz0w+qYdBSkdHvyfWoel1wLJo0SLWrFnDypUrD2tBmpqaKCwsxO/3Y7FYeOSRRzj77LO73H7hwoXcc889h7UMouciNSwyQkgcLbrRVqvXabrxTnUfTHvq25pUMgqnM3TyFCxWG2p4Qs+gz4cWDKFarVjtNla88RRKsAWjj01CX9Z8yZ6mPaQ50nhu3nMUJbclCKvyVjH3RbNW4+7P7ubPZ/25V8f+4oSFVH75ILPVjQCcFliGx9tCkivxIHuaRg4p4J17LufHf3mFlyudLG1OYemr+9s2+KyZlx02Jo81L7wTtq1hy8hJoCgY4e/ZUMw5lg1FQbOa39H6jHzOWrmRdKeXvRSahzLMET15zr6fP1yZQ5hw9eN4a37CjiX3Mnrny8yrXYZ1yy+5Yfy9/LS5HpqBRBVn0GDDqCSG72/F6tdw6JDzwi72v7ALG3BeipUGu8rgK8YASjQHiqEbZI3pOLOUEb7Ud9F6d1DDCooYdkUR+uU6b368lE2v1ZLUksEXj1Xw5xF/4cuszw58QxQDUMEeVBmp57PFVoZds/Cdr9zWhxIcBzUspaWlLFiwgCVLluB0dt8Lu7eSk5NZt24dHo+H999/n1tvvZXhw4d3aC6KuOOOO7j11lujr91uN0VFRZ1uKw6/UHiUhFVqV8RRohltHV0PWsPSxT/L311xQbttbGC0GwqrhTjzuos72avNsi9X4NyzCtXat4DFZXMB4A16+bLmS/JcedhUGyE9xIbaDdHtchJyujpEl6659Ktw6Vd55jc3843WZxmllvHBb+Zx1i8/PPjOYXa7jYd+eBmnvPM5P/qwPrrcpgcJqjYue2oDkxKXg2Fw5tY3OXvZa1z+s18zeILZ98O80Ovomo5f01i7v4zvbCulNjGNJnOWIgBSQ+HhzR/dR+O2VzAsNsqqaqj3OBn7lZ+TPe6UHpfZlT2ccdc8yZbN32DYf67g3LrP2PjZJXyYMZOAaqPZ4qI1cyanXXM1Lmcyb/5zHZO2xPbKdagKeSGDhOGpJLt6kP010lzU14glTFVVLjjzDGZNrefxP7xBamUhJ+24AsXvZF3hB20bKhBOn8NIvYAXbnqLspq9YBgU5nTeFNWdtoFw8XX+7lXAsnr1aqqrq5k2bVp0maZpLF26lIcffjhaO9IXqqoycqQ5edjkyZPZsmULCxcu7DJgcTgcOBxdpyoWR1ZkSLNValjEUdKbgMVTZQYijlA3tQtG7GRyJ15yycELEZnZuY/9toozipkzeA7vl7zPHcvu4J7P7iHDmUGDvyE6gd2J+Sdy16y7+nR8gBMuWgAvmKNEzlLX8Ph93+OmOx/u1TEuO/ckpo2r5OdPf8hJI7N5fW01m8khpFhZ02qO6pmOBSsaDUGVSI8bRVFQFAuqasFqs3HKyBEsKyjg9k/+hsu6h9kspY4sko1w/4vaWqj9GGg3aOnf89jjmgKDT8aZmGTOd6NawWI15/RR2x6x2FDCP1mqle3jrmPSpr+REXJzafV7bR+o7EVY/2M+O/svnP/Nb1Bd34KhG6xYuo8pK9pmUXa7fQcNWDRNp8Kq43F2Pjy+L7JSM7jtp1fw8CP/xrm5gFklF3PT3IsZO3ocwVCQYNBPSAsSMkKMLBgHQGH20EN4x3hrDDL1KmCZM2cOGzZsiFl2ww03UFxczO23397nYKUzhmHE9FERA0sonLPAIjUs4ijRIsECykHvbP2tbcHIyo0bots704rxNW4lIWM4xRecYR5NUUhMT8bICLF+W9sEgapqwWqxxTQ/Gd5qswx9DFgUReG3p/2Wv2/8O89vfZ56Xz3l3nIA0hxpXD76cm6adBOq0vcbgXGjRrBGH8lU1eyXcVPgXzzygAvD5mLORd+keMSQHh1nWFEe/7rrKgDOO7GUxUs34A+E0DQNRbWYE0UaoNi7r21PT0zgsbO/x6ZVf6Wq+RXSaaA830GqOwR5kzCMIGghmuoqSFE8ZDQEGeZdC1vW9vqzZx9k/dBlv4LZ3yAnIxFfSGPcqrqY9c1lHgLtft/W9ARUe+zv4ofvbubfp5uJA9MqdU7Q9MPSNO6w2rn5hq/y5P+ZnbEnj5hOcvtEgUdEfJ2/exWwJCcnM2FCbAcjl8tFZmZmdHl9fT0lJSWUl5t/hNu2bQMgLy+PvDwzzfN1111HYWEhCxcuBMz+KNOnT2fEiBEEAgHefPNNnn76aR599NFD+3TiiInUsNgkYBFHSaSGpSf9V1zZbU02Kx5u66cSbDWbZFrrd7P26d0d9juYyCjSJq281/tG2Cw2vnPCd7hp0k3sb95Pg7+BFHsKg5MH96JvTtcsVivei57E/9ocHIoZuN3c8lcAap5+gpfPfYtLTj54R9H2hg8t4kdDY5vc7/vQzKoaCh08J42iKKRmjKEq3BKza5j5exiedxvDxpmTI6bpOkvevY50/zIKy3201OZgt6agoKEaGoqhhx81VNpeq+jR106jFQexydB8ip1P0qeSGmrmzazTePPNNUzCiktV+FFsKhWSXtxFdfsFqkLBz2ehOs1LZZPHzyvWIJEL/d15GncvXY/Lr/PcmCGcOKxvnV8jGiobADCMEK70I5dNWTke+rD0xKuvvsoNN7RNbnXllVcCcPfdd/OLX/wCgJKSkphhYl6vl5tvvpn9+/eTkJBAcXExzzzzDFdcccXhLp44TELhJF4WSRonjpJIp9uejBA6YfxoPshbj7W+7aRvzohbiBHaTkj1oCua2ZEROu2DqBig6AekXgf8No3a7EOvUlcVlcEpgxlM50OYD8Wp00/gtab/UPLFy8wPPhVdnq24+Wzpu70OWDoVrrXy+oMH2dBUMOQUdmw8Gb++Cd1oxZkWYNvO28kbMosEVyaqqjJnzt95992v4Rm9ldDwEMVjHmTIkJN6XKS9T9/M0N2xSdOezz+fO0b9MGbZPnScIYNbFLB296vUDQLlXpzDzVwoL28oxx8+5ym6gRG+YfM6VN7aXceIrCSsqoLVomBRwo+q0mFYdFcaKxvNYyv+Hu9zKIw4O30fcsDy0Ucfxby+/vrruf7663u1z7333nvYh0eLI6ut0630YRFHR73P7ATq70HuDofNwU9+cU0Xa2/qcxnm/ut/qdI/47SMnmbZ7T8XzjkT5pxJ7d2LyVLa0j4MzThMff/CzVYeb2uPNrdY7Zx18b8AaG4s49NP5mJLauWTJTdw9ldfBcBmdXDWmc/z/gcX4HSUsXXlTSTYXyAnf1zPitTJsoeLru50W59V4a5JTiY1mjVEFQkq/y20svW0SSQ4rOz/ybLwlm0RzeL6JkiEH5LA908ZQWtA47IPN7MlWeGvagt/XbWl2/KdtKWVuetbMQyNA7ITRj+BolhRLT0LAg9dfEUscrURfSJ9WMTRVttae/CNjjA90ix1CH1MjrYXnZcRMtrKe3P5HXy5edshH9efZPYYqdy1s9f7JqcVMqzwZxgGqKmbWPfpQ9F1Na88zOAH68m7xcbgn4You/pKPDU1XR+sHUPr2Dy1bPmvubmxMWaZRTe42GfhmuJ8Hhvp4A/FTvZl2Vk9cywJDvM+3qfCe7lWvrNhL3PeWseoJWtZHu7D/fWx+SQ6rGQmOzgz2YXSw7mldueZTZWKYkFRrOEfW7sf873TDtYZ55BFhzkd6Tc6rGTyQ9EnmgxrFkdZgvXgc9wcaQEtBCpY1Pg5dX7njj+wdNX12F79Diep5qR3I144jWcn/dUcCt1HCUUjoWEvlTu6r1WI8LgrqCr5nLqa9XiatxMI7ceSpKBYDaqaHiUYuBGb3YX73XexlmtE7v6dFUHWf+vbnPjfxTFTDXTG3tix9i2RL7l988/JP/MpHvA20WxV0FSFV5warzTWgtV8n7EOO1mpbf/GTjn7wEzK5nZTWmBYXltn2J+fOZq7NJ2gbhDSdTTNbDLXdIOQZvCDD5bzSUYaAJd95mHi6VZGzRgBGOh6ZBoIA0M3c79YbBbyRhT06DvtMz2SyTl+/h2DBCyijyJ9WKzHUB8WwzDQDdANcyZqI/LcMDD0tud6OKmUppvP9fA6I3wM8xHAiE5LE9nHfN02d03kPbraVzdilxuYBzS62Jd2xz9w3/blOXBfiC1b+33bHzPms4R3ipYt/Dyy74HHbL9v5PtGDzG28jVcgZpoGc0nHR9bgy18ryEDi6Kw+HdfQwkfSInZtn2HQiO6DRjR7ZTouvD7QcyxovuHt1XabfMTzYeCC3b9h/dXvBazfcy+Ee3vYA0jmmCt7Z2jxY5dFhUAgljQUTFQw+8Rea1gYDHa1qno4fVtr1UMTlOqY+rTkxQfNZsfY2bTs2g6GIaCioKiqOFRWCoKKhl1O0n0wZSa8SQNm47NZmPEhIl4vV4CzU3YgEBNWYdSR6xZ8g34x0rUlnB/CRVsCqSbXyyGYjHLpcD2/3wFVAtGTces5ek7drDqjDMxxowBVQWLxXxs91yxqOTtUal1ZpM6xIdqswAqBhZ8rRO5otXKmcFkHqisZ1WGNab/hlMzOL1Fp/mT8Gc54LQ2QlepVnSaFRifn8Kz5XXRTaoDQdwhnVEuR/jfixL9tSvA2kRzmPSMdcu47KuTUFUVd43Z4VsJzx4eeVRVFUvITunGdrWJ7ftVxdSImNsrFgvqgaOUjLbJQs1dIu8BiqISavVCggQs4jgRqWGpbPJz35tbohfvmAu5AXr75+GLfUwwoBvRC5xmtHt+kGNEgoTOtjGP39nxwvvoBxzLiL2QiqPnVHU937Pf3+Ptz4o8OVpN/F1R6Vkqi6Pxb6oP9wzLGcorOfW0Wsqgmz7M3lzzMadhJyOWVxICNn7yXwBc4W2SvNU0NjWTltpxbqfgR+tJWdvT5rPum33MnC3dNwu2AC3YaGr9Oo7RB8z39O4+nMDPgc5n8G6haW1D28tz2z7PLrVtONEzTW6eaerFVDBOsx3JGgry5p9+2/P9jqCvD44EM/HTtAkSsIg+coZnYa31+Hl8ae+Hh8YzVQFVUcwfte15+EbGvMtSzLssBXN9211OZL15J6YqbXdN4Zus6B1aZN+YY3LANu2XdbW8y327et7Nvt0ch/D30tm+HdeZ+05u3Aal0GTLZmvK7PAXGLk9DZ9MlfDtOAqNgSb8WiB8d2zWaSgo5mslpj6k7UuJXNGjKeTDv49264zIXW5n+7bbzqcZNLcqpNizsKhq9P2MdvUrXdWiRL4TlEilghI9vBr9txP7/a1pfJ8QGoWJxVgtTkDFUFUMRQVFDafCV1EUC4ZirlMU1fysilkPo2NBR8Hf7MGdmEHWyCIMQ+d8whM9YuAPabjsKiFDR9M1dEMnpOu8VPIQAAG7irfoBPS6cpy+JoKOZEIGpPjq8NpT0LuI9i3u0cAmWocnknLpmdisiRi6Brre7rHtefhOBEPXCO4vQ9tWBgk2QvZsggXFoOkx+6JpZpWeZr527t6LrboSNVXHNSs//N3HVmEZMa/bnh+4/Lf1Gu9lqFiyEtrtbj6LtOQYwHt1bgY5bYxIcHJgC7lhQFNNFd59uxm/fR1F4yehaxqKqrSVJdIchIGhaWjBYOy/nciBImWMLjLC352Orkf67kQKYKCqFvOlQXhCz/AnMAysNrMvTcag+MoOLwGL6JOZwzK46/yxVLl9qGr44q2YnXCVyHPFnBhRiTwPX7gt7bZvv2/kwm8J7xN5HrmoW8IBQvT5AcewhNep4fWdHkNt915q7Pt2KL/arsztyn+o6bhFO19uhlJIHTyRE6/9Z3+XZsC5/7k38QQ9vPHVn3U5g/ORtPOJl/nSvpfWIjtDx+RjkBezPqTrZCgKr638J0ZTM5mfb6WQdNTw0HNjo5nVRM8sZuS3Hzzi5a26/zfUP/UUrvFZpF8y8pCOdW3459CNAE6G//nGYTnaYfH3c6G0Dkei6+DbDiASsIg+sVlUbjxt+ME3FKI7kRmY46gT69EUSSQXMkIH2fLIcKhm/4v1yWWsL3+s220v/UTnxGWxmdgiA6jVpCOXBC1GpAZCbiq61xpu+nIc6Uy6h5ecJYQQ/UcClm5FkuSF9P4JWL4x9QZqVvw+JmBS2v2/rZEEspobAJ2aTAcJWeGmBsOAhARG3XLL0Sqy6Alfo/mYeGiZeY82OUsIIfqPBCw9YvRTj/Azp1zAmVMuOPiGwOsrvwLsxXveTE772eNHtmBdinxPUsPSLUt4gsd+CoT7Kr66CAshji2RzoISsHQqoJnz4jit3U8wOCCEO4uqjsOUSVccOZbwXFtaoPvtBhgJWIQQ/UdqWLoVmYbAYRn4QYAaMAMWi6P/gitD+rD0TOTvTWpYhBCihyRg6ZJu6AR1MwiwR6rwBzAlYP4uVWcc1AYd78ITiRJneVjiq7RCiGNLNGA5+AzMx5tIsALxUsNi/i4tjn6cQiHahUVqWLplxGdfHwlYhBD9R5Malq6UNbelvI+HGhY1aPZHsjr7f84ncTCRprP4CgHkLCGE6D/SJNSlTXWbos+tSvffTyAY5JkXX8dd50NR4dQzJzF17PgO2xmReaDC01dEnkeyrhp6ZCoLHW/AyxcrN7B7XXieJyOcjdU8Utt8UeG5onKzrqXFVkOuI/HwfQm9Fe3D0n9FiAvRJqH4+qLkLCGE6D8SsHRpeu70g27T1OJme+keln+4FWVdNjZSAfh8fRXL1FIUw5zMEENB7WOFup2e5erwJufhTYa82oo+vY84iuK0SUjOEkKI/hMJWOJs1tijIdHWVlMRMkLYFBuNVS08e/cXAHidDST4UlFRUcjusL9V73szkoE5x1DIEiCQ4SZlLCiqgoIanhNKCc+X1bYs8IZOwJFKaqAfm69klFAPSZOQEEL0juRh6VL7fitBLYhNteENeqPLXL50AEJqEF+iG90WZOSpmZw1eyY1DQ3t5m9UCM+HiBEzr2PbcsLzZNksViyqBZvFRpI9qVedff/x6nMESGXdCg/btjwDihGeyrHDHJSAwqBxWUy+6dxD+o5EH8Vp05mcJYQQ/UeahLpkU23R55ERQ5nZsXO/eAdVErL4wVBQdJVdq2torP2AG6+79KiWFcBuV2gB6hKGQA/Se5Ss1hjX3Io9+TB20g1fiGWC0oOI02HNcpYQQvQfCVi6pLS7/Y10cHU6HPiLK3BszQfAtT+vw36BMqi+sI6c9KM7T8y535/Jzre/RNeNaCdds6Nu5Ppodu4N+fxsbijEUCxoIb37g/Za/0xhEHciGW7jYPRZe3KWEEL0H8nD0qX2tQRGuwvxORfM4PX6VSh2A1eWleQkl3mjrELzR2a/l6fv/gRD0aNV/iGXj2/fdg45GUcuiMk6YQTpE4ZisXT/u2ytqmXz3esBsDhs3W7bd1LD0i0JWIQQopekD0uX2tew6EZbTcSEkaOZ8MvRne7zm1X/IcmTSUIgOXaFL5Xla9dz4Zwzj0hZAR5Y+CyO0ixaUxtIyLSQlGUnOy+VoqI8xgwfQmKC2fSj+doS4lkch/f33l+TRMYdLfw7kIBFCCF6KJLNVQKWDrqqYenOzb/8Cpt27sQwIBAIUlJSSeXSEIn+VFTLke2vYNufjlW3kdyQAw3g2wml6JRSzieU4kmqR832k+WyAbkAqOoRqgmRPizdi9awHKkariNDzhJCiP4jfVi6pSoquqET6uEkdcmJScyaNBmA+29fRHJTDpHB0Tbbkf2OFcMMiPSZlYQ0jda6EEaTDWdzCs5QEimebPCAL7K9Hjz8nWOlhqVnDBnWLIQQvSMBS7dsqg2/5o/O2twbzmYziVyLzU0opZUTT7jgcBcvhkU3+65ceN5pDC0YFF2u6zr7qyrZuHUXJbur8Oz2kLE/gdTGTcDhHdast7SYT6SG5ZgkZwkhRP+RPizdivTJsKs962vQ3OLh9XeX0uL1A2afkTk3j+o0TX9ndF2ntrEBlzOR1Zs2EgyF0I1IGv926fwPfK3rqJgBi8Mem7tFVVUG5xcwOL8AzoSdaz4kePXNtDhgy+cngqKaNS3hRHSqakFRVUABVUEJr1dVC0o4cYyiKCiqiqKoqKolusz96mvm52hu7tHnPX7FZ02UnCWEEP1Hali6FTLM78faw+/nhcVL8H+SBjiJ9E5Qe1jbUN1Qx5O/fp9kT1bvC9pOotPZ7Xot3OEz0Q/c8COg7fJpAIdjoHO1pzLcS0Z0L75qouQsIYToPzKsuVtKLy8ovpYgCuBNbICcVpJz7EwafUa3+9Q1NvD8oncxvkwn2WgLVlptHjRHpCnKwIgURWl3d67EZkx1DYPUpANGKB1g2KRT+KA4nZTyprbdo48GikEny8110edmkaKParvtmhOgbHYRE7stxXEuTvv6SMAihOg/UsPSLVVR0Qytx51uVVUxE7VltHLyucVomsYHqz5H03U0TSOkmcfSdZ3G8lY8nyVi0+0oZEVDI5+9mSlX5jF7xmwctp6n5u8puyOR817+7LAfF+Bnn/6Ml3e+zIKi1CNy/GOCrhOt04qzGwU5Swgh+k+0D0t8nTiPhoAWiKbkbz8RYndUq4oGJO0vYP1j7k62UCDaWOSg/aDWxLOamD5pPONGjTho8reBKtLXJ6gFD7Llcax98BtnNwrxVVohxLFFOt12qX1zkNrD4aczZhbz3sYtWII2jGjbCW1tJqq5LDIZoZoRxJmpctlX5xzRLLhHi2aY/54sEgB3TW8XzEkeFiGE6KHI3Z4iF5gDte9o6w16SbZ33zcEYNq4CUx7cMKRLNaA1hIyhzU7Ld13/D2uta99irMbhfjKGiOEOLZIH5YuKYqCJRzIScr5nvGFzLR0TqsELF1qX6sS6n1+n/4kAYsQov8Y0iTUnUgti35YBvse+yTA6wG7Cxwp5nNPVf+WpZfkLCGE6D/RPixy79SZyAVY14/fgKW2tZZ6Xz2GYaAbOgZG9PnQ1KExTWX28GR+fckMfFxxJIPfDQFvf5ekVw4pYFm4cCF33nknCxYs4KGHHgJg8eLFPPbYY6xevZq6ujrWrl3L5MmTuz3OE088wdNPP83GjRsBmDZtGvfddx8zZ848lOIJIQY66XTbrUjn0UgCuePNlrotXPnGlTGzVXfmsbMf4+SCk3FYzGHYAT1wNIoXvyKdkiN/f3Giz2eJlStX8vjjjzNp0qSY5V6vl9mzZ3P55Zdz44039uhYH330EVdddRUnn3wyTqeTBx54gHPOOYdNmzZRWFjY1yIKIQY66XTbrUgNS2lzqZkGn9hahmhtAzoYtK07YD3Ezvjc2bL2Ot3nYPvHPO36Pbt6386O+UXFF+iGjk21kepIRcVM098SaqE50JZ+f/5783nw9Aepbq0GzCHhohuRv7ce5vcZKPoUsHg8Hq655hqeeOIJ7r333ph11157LQB79+7t8fGeffbZmNdPPPEEL774Iu+//z7XXXddX4oohIgHdTvMx2Br/5ZjgIoELPPfn9/PJelf5w49l4WnLoxZpukap//7dJr8TYSMELd8dEt0XU8T7R231OMoYJk/fz7z5s1j7ty5HQKWw6GlpYVgMEhGRkaX2/j9fvz+tnZKt7uzJElCiLjgl7/fzlw04iJe3PEiYOZiUVCiOVkirxXCkwEqSrQGIrqs3fYHUtrNMRTJ+dI+94vSyRxEne0Ts38X6zs7Rmf7d7bebrFzxZgrOhzHolpYNG8Rv1n5GzwBTzQjsNPq5Lxh53X63iIs0nfF7urfcvRSrwOWRYsWsWbNGlauXHkkygPAT37yEwoLC5k7d26X2yxcuJB77rnniJVBCHEUJXZ9c3I8u3X6rdw6/db+LsaANSh5EH8+68/9XYz4YhjQUmc+T4yvZIG96ppfWlrKggULeOaZZ3AeZEbOvnrggQd4/vnnWbx4cbfvcccdd9DU1BT9KS0tPSLlEUIcIe2HnuaM779yCHE8CbZApI9PQlq/FqW3elXDsnr1aqqrq5k2bVp0maZpLF26lIcffhi/339Ic1A8+OCD3Hfffbz33nsdOvMeyOFw4HAc/om5hBBHSfuOkVb5WxbiqGg/4irORuf1qrRz5sxhw4YNMctuuOEGiouLuf322w8pWPntb3/LvffeyzvvvMP06dP7fBwhRJwIZyUFQDKTCiEOolcBS3JyMhMmxM5T4XK5yMzMjC6vr6+npKSE8vJyALZt2wZAXl4eeXl5AFx33XUUFhaycKHZ6/uBBx7gZz/7Gc899xxDhw6lsrISgKSkJJKSkg7h4wkhBqxoWnAl7iZhEyJ+tesMHWcZgQ97eslXX32VKVOmMG/ePACuvPJKpkyZwl//+tfoNiUlJVRUVERfP/LIIwQCAS677DLy8/OjPw8++ODhLp4QYqCI1LBYneHpg4UQomuKcYxMuuB2u0lNTaWpqYmUlJT+Lo4Q4mBqd8DD08GZBj/Z19+lEeL4EPDCfQXm8zsrwJ7Yv+Wh59dvmcBDCNE/2tewCCGOjjiuo5CARQjRPyJ9WGSEkBBHT8woofiaEkMCFiFE/5AaFiGOPqPdhIddZEEeqOKrtEKIY0c0YJEaFiGOGr1dDUucTToqAYsQon9Em4SkhkWIoyamSSi+QoD4Kq0Q4tghNSxCHH2RJqE4aw4CCViEEP1FaliEOPr0SMASX81BIAGLEKK/SA2LEEdfpEkozkYIgQQsQoj+IsOahTj6pElICCF6SYY1C3H0SZOQEEL0ktSwCHH0RTLdxtkIIZCARQjRX6SGRYijT5qEhBCil6SGRYijT5qEhBCil6SGRYijT0YJCSFEL0kNixBHnzQJCSFEL0kNixBHnzQJCSFEL0kNixBHn4wSEkKIXorUsFgkYBHiqJEmISGE6CWpYRHi6IsEKrre/XYDkAQsQoj+IQGLEEef3WU+Bjz9W44+kIBFCNE/tHDAIk1CQhw9wVbz0ZbQv+XoAwlYhBD9IxQwH6WGRYijR1HMx0jn2zgiAYsQon9o0iQkxFGXmGU+emtAC/VvWXpJAhYhRP+I1LBY7P1bDiGOJ85U81EPgi4BixBCHJwmAYsQR137IEW19l85+kACFiFE/9CD5qPF1r/lEOJ4Esl0C3E3n5AELEKI/hE5ccbZXZ4QcS1Sw6JY2jrgxgkJWIQQ/SNy4pSARYijJ47/7iRgEUL0jzg+cQoRt+L4704CFiFE/4ieOOOrHV2IuBbHTbESsAghjj7DACM8l0kcTnMvRNyK4xsFCViEEEdfHI9UECKuSZOQEEL0giEBixD9QgIWIYTohfY1LNIkJMTRI31YhBCiF6SGRYj+cbz2YVm4cCGKonDLLbdEly1evJhzzz2XrKwsFEVh3bp1Bz3Opk2buPTSSxk6dCiKovDQQw8dSrGEEAOd1LAI0T+Ox4Bl5cqVPP7440yaNClmudfrZfbs2dx///09PlZLSwvDhw/n/vvvJy8vr69FEkLEi8gIIYjLE6cQcSsasMTflBh9asTyeDxcc801PPHEE9x7770x66699loA9u7d2+PjzZgxgxkzZgDwk5/8pC9FEkLEk5gaFmmZFuKoOd463c6fP5958+Yxd+7cw12eHvP7/bjd7pgfIUSciPRhUdS4m89EiLgWuVmwxF/A0usSL1q0iDVr1rBy5cojUZ4eW7hwIffcc0+/lkEI0UftJ2ATQhw9kVnS4/Bvr1c1LKWlpSxYsIBnnnkGp9N5pMrUI3fccQdNTU3Rn9LS0n4tjxCiF6JDK+PvpClEXIvcLFiO8T4sq1evprq6mmnTpkWXaZrG0qVLefjhh/H7/VgsR+cE5HA4cDgcR+W9hBCHWbRJSAIWIY4qLVzDEod9WHpV4jlz5rBhw4aYZTfccAPFxcXcfvvtRy1YEULEOT08SigOT5pCxLXjpYYlOTmZCRMmxCxzuVxkZmZGl9fX11NSUvL/7d19cFTlvQfw724S8rJ5gbywMYERhciLFBPiXCS2grwEFSSVOwU0EyrWDh2gDXW80mgqcIeyhaESO/RFrI6F2IaionRo1UQQpeTGGFguiW0gBU0MieEKJgHi5mV/94/Nrtm8kN3knD1ns9/PzJmzec7u4Xl+s9n8+J3nPIuLFy8CAKqrqwEAiYmJrluWV61aheTkZFgsFgBAe3s7PvnkE9fj+vp6WK1WREZGYtKkScMYHhHpkrPCYuQdQkQ+5aqw+F/CovinxaFDh5CWlobFixcDAFauXIm0tDT8/ve/dz2ntrYWDQ0Nrp8vXryItLQ0pKWloaGhATt37kRaWhoef/xxpbtHRHpg5yUhIk348W3Nw+7x+++/7/bzo48+ikcffdSr10yYMAEiMtyuEJG/EE66JdKE65KQ/yUsrMcSke+xwkKkDV4SIiLyAm9rJtKGcx0WP5x0y4SFiHyv50q3ROQ7fnxbMz8tiMj3WGEh0obrd48JCxHR4LhwHJE2eEmIiMgLrLAQaYOTbomIvCD+W5Ym8mu8rZmIyAvOpfk56ZbIt1hhISLyAheOI9KGs8Lih5iwEJHvceE4Im2MMjn2tlZt+zEETFiIyPdc32fChIXIp75uduwj4rTtxxAwYSEi3+NtzUTaaLvs2EfEatuPIWDCQkS+x9uaibTh/N3jOixERB4Q3iVEpAm7/34thv/1mIj8HyssRNrw4/8s+F+Picj/cQ4LkUake2/QtBdDwYSFiHyPFRYibUh3wmJgwkJENDguzU+kEVZYiIg858cT/4j8GissRERe4CUhIo2wwkJE5DlOuiXShqvC4n9//v2vx0Tk/1hhIdIILwkREXmOFRYibTgrLH6ICQsR+Z6rwsKPICJNsMJCROQB12qbrLAQ+ZTzd4+TbomIPMA5LETa4G3NREResHc69lw4jsjHeFszEZHnOOmWSBussBAReYGXhIg0wgoLEZHnmLAQaYMVFiIiL3R+7dgHh2vbD6KAw5VuiYg812lz7INDte0HUaARXhIiIvKcq8ISpm0/iAIOLwkREXnOlbCM0rYfRIGGFRYiIi90tTv2rLAQ+ViAVlgsFgsMBgM2bNjganvjjTewaNEixMfHw2AwwGq1enSu119/HdOmTUNoaCimTZuGgwcPDqdrRKRnvCREpI1ArLCUl5djz549mDFjhlv7tWvXcPfdd+OXv/ylx+cqLS3FihUrkJOTg9OnTyMnJwfLly9HWVnZULtHRHrGSbdEGvHfCsuQ1sW+evUqsrOz8eKLL2Lr1q1ux3JycgAAn376qcfnKygowMKFC5GXlwcAyMvLw7Fjx1BQUIA///nPQ+kiEekZKyxE2gi0Csu6deuwePFiLFiwQJFOlJaWIjMz061t0aJFOHHixICvsdlsaGlpcduIyE+wwkKkDdfCcdp2Yyi8rrAUFRXh5MmTKC8vV6wTjY2NMJvNbm1msxmNjY0DvsZisWDLli2K9YGIfIgVFiKNBEiFpa6uDrm5uSgsLERYmLIfNIZe19NEpE9bT3l5eWhubnZtdXV1ivaHiFTU6bxLiBUWIp/y46X5vaqwVFRUoKmpCenp6a62rq4ufPDBB9i9ezdsNhuCgrz/bpDExMQ+1ZSmpqY+VZeeQkNDERrKDzsiv8QKC5FGAmRp/vnz5+PMmTOwWq2u7c4770R2djasVuuQkhUAmD17NoqLi93a3n33XWRkZAzpfESkc5zDQqQNP55061WFJSoqCtOnT3drM5lMiIuLc7VfvnwZtbW1uHjxIgCguroagKOKkpiYCABYtWoVkpOTYbFYAAC5ubm45557sH37dmRlZeGtt95CSUkJjh8/PrzREZE+OSss/3sACIvx7DVKlbANBuDWe4G4icqcj8gXRIDPTgDNdT2SDvnmWM+f+2tz/tx2xbEf6ZeEPHHo0CGsXr3a9fPKlSsBAJs2bcLmzZsBALW1tTAavynuZGRkoKioCPn5+fj5z3+OiRMnYv/+/Zg1a5bS3SMirdntjnK0dAFHtw7+fDUkTAXW/Y82/zbRUDSeAV55QLnzBflfddMgIjL40/SvpaUFMTExaG5uRnR0tNbdIaIbObkXqHnPgyd6+PHk6ceYrQU4/z5gSgD+q8az1xDpwbkS4NX/dDyeOL9XhaT7sattgJ+dbaNvBjK3AkGK1yyGxNO/3/roLREFlpmrHJuvffEJ8LvZvv93iYars82xH/cfQM4b2vZFI/43TZiIiCjQXL/s2EfEatsPDTFhISIi0ru27oQlfIy2/dAQExYiIiK9c1ZYwllhISIKHCPjXgMKJM4KSwQrLERERKRX17vXT2GFhYgoAPjhYllEAHpUWJiwEBERkV5xDgsTFiIiIt1zLqnPCgsRERHpkkiP25qZsBAREZEe2VoBe6fjMSssRESBhLc1kx9xVleCw4GQcG37oiEmLEQUQHiXEPkhLssPgAkLERGRvnH+CgAmLERERPrmXDQugFe5BZiwEBER6Ru/+BAAExYiIiJ946JxAJiwEFEg4pcfkj/hsvwAmLAQUSDhdwmRP2KFBQATFiIiIn1jhQUAExYiIiJ9u9rk2EfEadsPjTFhISIi0rOv6hz7MRM07YbWmLAQERHpVUcbYGt2PI40a9sXjTFhISIi0qurXzj2QaFAWIy2fdEYExYiChzGYMe+q0PbfhB5yjl/Jcoc8He5MWEhosDh/MDvbNO2H0SeclZYAvxyEMCEhYgCSUuDYx/gS5yTH2ltdOyZsDBhIaIA0nHdsY9O0rYfRJ5yXRJK1LYfOsCEhYgCh3PuijFE234QeeoqKyxOTFiIKHCI3bEP8MmL5EecFRYmLExYiIiIdItzWFyYsBBR4AgOc+w7vwbsdm37QuSJnrc1B7hgrTtAROQzo0yOfeMZ4L/HADA41mZxbUHD+zko2DE/Jqh7cz0e1X08ZIDjHjw2BjvO43rsPG+I4991PjYG8ZLXSGG3A9d4SciJCQsRBY6xU4Gom4DW7tubIYC9w7GNGIbuxGbUN0mN83FwaN+2fh8Pdjy4VyLVK3nq91ivhK33MSZZfV3/ErB3AjAAprFa90ZzTFiIKHCEjwY2VALtVwF7l+OPgdvWu60LkH7a+vu5qzvx6ers3rcP8Lijx3O7j7m9vsP9XF3t/T92nld6X9oSoMvm2PyJsXei06ua5JYMDZA0DXqsv6pVf8d6vt6LY0pXt5yJtSne8e8GuGFFwGKx4Omnn0Zubi4KCgoAACKCLVu2YM+ePbhy5QpmzZqF3/zmN7j99tsHPE9HRwcsFgv++Mc/or6+HpMnT8b27dtx3333Dad7RER9BQU7EpeRwm7vkRT1THa6f+60fZMYuZ7T+7EHz+ls/6ZtwASr48bJl7NNuvoZR3cC6O+rEHuU6HiYkF37P8c5I7kGCzCMhKW8vBx79uzBjBkz3Np37NiB5557Dq+88gpuu+02bN26FQsXLkR1dTWioqL6PVd+fj4KCwvx4osvYsqUKXjnnXfw0EMP4cSJE0hLSxtqF4mIRj6jETCGOi73+AtXktVPMuOW/NzoWM/EqFcFy3msv+RqwGMDVMNudAzSz9hUuMQ45mZlz+enDCLST8Rv7OrVq5g5cyZ++9vfYuvWrUhNTUVBQQFEBElJSdiwYQM2btwIALDZbDCbzdi+fTvWrFnT7/mSkpLwzDPPYN26da627373u4iMjERhYaFHfWppaUFMTAyam5sRHR3t7ZCIiIi8Y+8aJDFq77/qdKNjvZ8HAzBjORB7i9ajVY2nf7+HVGFZt24dFi9ejAULFmDr1q2u9gsXLqCxsRGZmZmuttDQUMyZMwcnTpwYMGGx2WwICwtzawsPD8fx48cH7IPNZoPN9s012paWlqEMhYiIaGiMQY4tJGzw59Kweb0OS1FREU6ePAmLxdLnWGOjY4Ebs9n99iuz2ew61p9Fixbhueeew7lz52C321FcXIy33noLDQ0NA77GYrEgJibGtY0fP97boRAREZGf8CphqaurQ25uLgoLC/tURHoy9JolLSJ92np6/vnnkZKSgilTpmDUqFFYv349Vq9ejaCgoAFfk5eXh+bmZtdWV1fnzVCIiIjIj3iVsFRUVKCpqQnp6ekIDg5GcHAwjh07hl//+tcIDg52VVZ6V1Oampr6VF16SkhIwJtvvolr167hs88+w7/+9S9ERkbillsGvmYXGhqK6Ohot42IiIhGJq8Slvnz5+PMmTOwWq2u7c4770R2djasVituvfVWJCYmori42PWa9vZ2HDt2DBkZGYOePywsDMnJyejs7MTrr7+OrKws70dEREREI45Xk26joqIwffp0tzaTyYS4uDhX+4YNG7Bt2zakpKQgJSUF27ZtQ0REBB555BHXa1atWoXk5GTXPJiysjLU19cjNTUV9fX12Lx5M+x2O5566qnhjo+IiIhGAMWXznvqqafQ1taGtWvXuhaOe/fdd93WYKmtrYXR+E1x5+uvv0Z+fj7Onz+PyMhIPPDAA9i3bx9Gjx6tdPeIiIjIDw1pHRY94josRERE/sfTv99e39ZMRERE5GtMWIiIiEj3mLAQERGR7jFhISIiIt1jwkJERES6x4SFiIiIdE/xdVi04rw7m9/aTERE5D+cf7cHW2VlxCQsra2tAMBvbSYiIvJDra2tiImJGfD4iFk4zm63o7q6GtOmTUNdXR0XjxtES0sLxo8fz1h5iPHyHGPlHcbLO4yX5/wlViKC1tZWJCUlua2C39uIqbAYjUYkJycDAL+92QuMlXcYL88xVt5hvLzDeHnOH2J1o8qKEyfdEhERke4xYSEiIiLdG1EJS2hoKDZt2oTQ0FCtu6J7jJV3GC/PMVbeYby8w3h5bqTFasRMuiUiIqKRa0RVWIiIiGhkYsJCREREuseEhYiIiHSPCQsRERHpnl8lLGfPnkVWVhbi4+MRHR2Nu+++G0ePHnUdP336NB5++GGMHz8e4eHhmDp1Kp5//vlBz2uz2fDjH/8Y8fHxMJlMWLp0KT7//HM1h6K6wWIFALm5uUhPT0doaChSU1M9Ou+///1vPPTQQ0hISEB0dDSWL1+OL774QoUR+JZa8WpsbEROTg4SExNhMpkwc+ZMvPbaayqMwLfUiNenn34Kg8HQ73bgwAGVRqI+td5bAFBaWop58+bBZDJh9OjRmDt3Ltra2hQegW+pFa+5c+f2eV+tXLlShRH4jprvLcCxAu39998Pg8GAN998U7mOD5FfJSyLFy9GZ2cnjhw5goqKCqSmpmLJkiVobGwEAFRUVCAhIQGFhYWoqqrCM888g7y8POzevfuG592wYQMOHjyIoqIiHD9+HFevXsWSJUvQ1dXli2GpYrBYAY4342OPPYYVK1Z4dM5r164hMzMTBoMBR44cwT/+8Q+0t7fjwQcfhN1uV2soPqFGvAAgJycH1dXVOHToEM6cOYNly5ZhxYoVOHXqlBrD8Bk14jV+/Hg0NDS4bVu2bIHJZML999+v1lBUp9Z7q7S0FPfddx8yMzPx0Ucfoby8HOvXr7/h0ub+QK14AcAPf/hDt/fXCy+8oHT3fUrNWAFAQUEBDAaDkl0eHvETly5dEgDywQcfuNpaWloEgJSUlAz4urVr18q999474PGvvvpKQkJCpKioyNVWX18vRqNR3n77bWU672PexmrTpk1yxx13DHred955R4xGozQ3N7vaLl++LACkuLhYkb5rQa14iYiYTCbZu3evW1tsbKz84Q9/GFaftaRmvHpLTU2Vxx57bKhd1ZyasZo1a5bk5+cr1VVdUDNec+bMkdzcXIV6qj21fw+tVquMGzdOGhoaBIAcPHhQgV4Pj9+k4nFxcZg6dSr27t2La9euobOzEy+88ALMZjPS09MHfF1zczNiY2MHPF5RUYGOjg5kZma62pKSkjB9+nScOHFC0TH4ylBjNRibzQaDweC2CFFYWBiMRiOOHz+uRNc1oVa8AODb3/429u/fj8uXL8Nut6OoqAg2mw1z585VpvMaUDNePVVUVMBqteIHP/iBYuf0NbVi1dTUhLKyMowdOxYZGRkwm82YM2eOX/8eAuq/t1599VXEx8fj9ttvx5NPPonW1lYFeq0NNWN1/fp1PPzww9i9ezcSExMV6vHw+c2XHxoMBhQXFyMrKwtRUVEwGo0wm814++23MXr06H5fU1pair/85S84fPjwgOdtbGzEqFGjMGbMGLd2s9nsVlbzJ0OJlSfuuusumEwmbNy4Edu2bYOIYOPGjbDb7WhoaFBuAD6mVrwAYP/+/VixYgXi4uIQHByMiIgIHDx4EBMnTlSm8xpQM149vfTSS5g6dSoyMjIUO6evqRWr8+fPAwA2b96MnTt3IjU1FXv37sX8+fNRWVmJlJQUhUbgW2q+t7Kzs3HLLbcgMTERlZWVyMvLw+nTp1FcXKxM531MzVj99Kc/RUZGBrKyspTprEI0r7Bs3rx5wIl2zu3jjz+GiGDt2rUYO3YsPvzwQ3z00UfIysrCkiVL+v1jWVVVhaysLDz77LNYuHCh1/0SEX1du4N6sfJUQkICDhw4gL/+9a+IjIxETEwMmpubMXPmTAQFBSk4UmVoHS8AyM/Px5UrV1BSUoKPP/4YTzzxBL73ve/hzJkzCo1SOXqIl1NbWxv+9Kc/6ba6onWsnHPG1qxZg9WrVyMtLQ27du3C5MmT8fLLLys1TMVoHS/AMX9lwYIFmD59OlauXInXXnsNJSUlOHnypEKjVIbWsTp06BCOHDmCgoIC5QalFM0uRnW7dOmS/POf/7zh1tbWJiUlJX3mT4iITJo0SSwWi1tbVVWVjB07Vp5++ulB//333ntPAMjly5fd2mfMmCHPPvvs8AeoIDViJTK0OQaXLl2SK1euiIiI2WyWHTt2DHVYqtE6XjU1NQJAKisr3drnz58va9asGdbY1KB1vHrau3evhISESFNT03CGpBqtY3X+/HkBIPv27XNrX758uTzyyCPDGpsatI5Xf+x2e5/5i3qgdaxyc3PFYDBIUFCQawMgRqNR5syZo9Aoh0bzS0Lx8fGIj48f9HnXr18HgD4z4I1Go9sdKlVVVZg3bx6+//3v4xe/+MWg501PT0dISAiKi4uxfPlyAEBDQwMqKyuxY8cOb4aiOqVjNdy+AMCRI0fQ1NSEpUuXKnJeJWkdr4HOGxQUpMu7qrSOV08vvfQSli5dioSEBEXOpzStYzVhwgQkJSWhurrarf3s2bO6vKNK63j1p6qqCh0dHbjpppsUPe9waR2rn/3sZ3j88cfd2r71rW9h165dePDBB4d8XkVomi554dKlSxIXFyfLli0Tq9Uq1dXV8uSTT0pISIhYrVYREamsrJSEhATJzs6WhoYG19bzf2mff/65TJ48WcrKylxtP/rRj2TcuHFSUlIiJ0+elHnz5skdd9whnZ2dPh+nEjyJlYjIuXPn5NSpU7JmzRq57bbb5NSpU3Lq1Cmx2Wwi0n+sXn75ZSktLZWamhrZt2+fxMbGyhNPPOHzMSpJrXi1t7fLpEmT5Dvf+Y6UlZVJTU2N7Ny5UwwGgxw+fFiTsSpBzfeX83UGg0H+/ve/+3RcalAzVrt27ZLo6Gg5cOCAnDt3TvLz8yUsLExqamp8Pk6lqBWvmpoa2bJli5SXl8uFCxfk8OHDMmXKFElLS+Pn/AC/hz1BJ3cJ+U3CIiJSXl4umZmZEhsbK1FRUXLXXXfJ3/72N9fxTZs2CYA+28033+x6zoULFwSAHD161NXW1tYm69evl9jYWAkPD5clS5ZIbW2tD0emvMFiJeK4za+/eF24cEFE+o/Vxo0bxWw2S0hIiKSkpMivfvUrsdvtPhyZOtSK19mzZ2XZsmUyduxYiYiIkBkzZvS5zdkfqRUvEZG8vDwZN26cdHV1+Wg06lIzVhaLRcaNGycREREye/Zs+fDDD300KvWoEa/a2lq55557JDY2VkaNGiUTJ06Un/zkJ/Lll1/6eHTKUvO91ZNeEhaDiIg6tRsiIiIiZWh+lxARERHRYJiwEBERke4xYSEiIiLdY8JCREREuseEhYiIiHSPCQsRERHpHhMWIiIi0j0mLERERKR7TFiIiIhI95iwEBERke4xYSEiIiLdY8JCREREuvf/0GYwdv11aYcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-81.57057476910849 41.321037282749415) 0 5195 cTcP, target_pCP, diToler\n",
      "here is a GOOD Cuya-Summ straddler for nSplits, ppC, mC skew ratios of 0 1.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-81.57057476910849 41.321037282749415) 0 5195 cTcP, target_pCP, diToler\n",
      "here is a GOOD Cuya-Summ straddler for nSplits, ppC, mC skew ratios of 0 2.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-81.41063871186118 41.342109214644154) 0 5195 cTcP, target_pCP, diToler\n",
      "here is a GOOD Cuya-Summ straddler for nSplits, ppC, mC skew ratios of 0 0.5 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-81.41063871186118 41.342109214644154) 0 5195 cTcP, target_pCP, diToler\n",
      "here is a GOOD Cuya-Summ straddler for nSplits, ppC, mC skew ratios of 0 1.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POINT (-81.41063871186118 41.342109214644154) 0 5195 cTcP, target_pCP, diToler\n",
      "here is a GOOD Cuya-Summ straddler for nSplits, ppC, mC skew ratios of 0 2.0 1.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All done creating Cuya-Summ straddlers.  We had 6 successes out of 6\n"
     ]
    }
   ],
   "source": [
    "print(\"here are two Summit-pairing choices for optionB, based on swelling in each SE corner. Model off optionA SummPortStark\")\n",
    "swellStarters = [4516, 5036]  #4405\n",
    "CuyaSumm = [17, 76]\n",
    "mC, ppC = CuyaSumm[0], CuyaSumm[1]\n",
    "#cutAngle = [1.15*math.pi, 1.2*math.pi]\n",
    "wholeDistrictShadeRatio = 1.01  #how much we're willing to nudge up Cuya and Summ whole districts so straddler doesn't go over\n",
    "tgtMCpop, tgtPPCpop = countyPop[mC]%(wholeDistrictShadeRatio*aDP), countyPop[ppC]%(wholeDistrictShadeRatio*aDP)\n",
    "tgtPop =  tgtMCpop + tgtPPCpop \n",
    "print(\"The tgtPop for Cuya-Summ will be\",tgtPop,\"vs usual\",aDP)\n",
    "toler = 0.05*aDP\n",
    "diToler = min(tgtPop - 0.95*aDP, 1.05*aDP - tgtPop) #a bit tighter since we need to add 2 partials\n",
    "skewRatio = [[1.], [0.5, 1., 2.] ]  #since we will use std HD on rest of Cuyahoga, just one in-Cuya shape per starting vtd\n",
    "CuyaSummDiLists, CuyaSummDiLists3, CuyaSummSplitLists3 = list(), list(), list()\n",
    "for iii, t in enumerate(swellStarters):\n",
    "    homeM = tractMuniNo[t]\n",
    "    for ppCxyR in skewRatio[1]:  #do Summit (ppC) first b/c it will be more constrained on its remnant whole districts\n",
    "        cTcP = tractCP[ getClosestTract(tractCP[t], countyTractList[ppC],tractCP) ]\n",
    "        print(cTcP, int(target_pCP),int(diToler),\"cTcP, target_pCP, diToler\")\n",
    "        ppClist, ppCpop = vtdSwell(cTcP, tgtPPCpop, diToler, tractCP, countyTractList[ppC],neighborList,tractPop3,ppCxyR) \n",
    "        ppClist3, ppCpop3, ppCsplit = muniSwell(cTcP, tgtPPCpop, diToler, aDP, muniPop, muniPCP, muniTractList,\n",
    "                                                muniNeighborList,countyMuniList[ppC], tractCP, neighborList, tractPop3, 0, ppCxyR)\n",
    "        \n",
    "        cTcP = tractCP[ getClosestTract(tractCP[t], countyTractList[mC],tractCP) ]\n",
    "        targetMCP = tgtPop - ppCpop\n",
    "        for mCxyR in skewRatio[0]:\n",
    "            mClist, mCpop = vtdSwell(cTcP, targetMCP, diToler, tractCP, countyTractList[mC],neighborList,tractPop3,mCxyR) \n",
    "            targetMCP3 = tgtPop - ppCpop3\n",
    "            mClist3, mCpop3, totSplits = muniSwell(cTcP, targetMCP3, diToler, aDP, muniPop, muniPCP, muniTractList, \n",
    "                                                   muniNeighborList,countyMuniList[mC], tractCP, neighborList, tractPop3, ppCsplit, xyR)\n",
    "            stradList, stradList3 = mClist + ppClist, mClist3 + ppClist3\n",
    "            stradPop, stradPop3 =   mCpop  + ppCpop,  mCpop3  + ppCpop3\n",
    "            if isContiguous(stradList, neighborList) and isContiguous(stradList3, neighborList):\n",
    "                if abs(stradPop - aDP) < 0.05 * aDP and abs(stradPop3 - aDP) < 0.05 * aDP and totSplits <= 1:\n",
    "                    CuyaSummDiLists.append(stradList)\n",
    "                    CuyaSummDiLists3.append(stradList3)\n",
    "                    CuyaSummSplitLists3.append(totSplits)\n",
    "                    for v in stradList3:\n",
    "                        plotPoly(tractGeom[v])\n",
    "                    plotPoly(countyGeom[mC])\n",
    "                    plotPoly(countyGeom[ppC])\n",
    "                    plotPoly(tractCP[t].buffer(0.005))\n",
    "                    print(\"here is a GOOD Cuya-Summ straddler for nSplits, ppC, mC skew ratios of\",totSplits,ppCxyR, mCxyR)\n",
    "                    plt.show()\n",
    "                else:\n",
    "                    print(\"FAIL for pop or nSplits, which were\",stradPop, stradPop3, totSplits,\"for starter, skews=\",t,ppCxyR, mCxyR)\n",
    "            else:\n",
    "                print(\"FAIL for vtd or muni contiguity for starter, skews=\",t,ppCxyR, mCxyR)\n",
    "print(\"All done creating Cuya-Summ straddlers.  We had\",len(CuyaSummDiLists),\n",
    "      \"successes out of\",len(swellStarters)*len(skewRatio[1])*len(skewRatio[0]) )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "11233fc5-8246-4cb2-957b-c0dd4fbdb982",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the CuyaSumm lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the CuyaSumm lists to a file.  We will vote later\" )    \n",
    "date = \"01Apr\"\n",
    "rLISTS = [CuyaSummDiLists.copy(), CuyaSummDiLists3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo = list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"CuyaSumm\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "bf08a91e-2dbd-44b1-be53-cd5cb8b4ba82",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: the below builds Summit 4-district sets for each Summit triArea.  10 tries / triArea, we store any legit 4-district set\n",
      "total 4-district pops for vtd- and muni-snapped should be 486650.0 482354.0\n",
      "0 1 iii,try no. 2-district pops were 242041.0 5412.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg= 0 373  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "0 2 iii,try no. 2-district pops were 242892.0 5412.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg= 0 391  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "0 3 iii,try no. 2-district pops were 243881.0 5412.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg= 0 409  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "0 4 iii,try no. 2-district pops were 288690.0 239631.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg 0 427 one of the 2-district pieces is discontiguous\n",
      "0 5 iii,try no. 2-district pops were 243604.0 238459.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg 0 445 one of the 2-district pieces is discontiguous\n",
      "0 6 iii,try no. 2-district pops were 243585.0 238197.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg 0 463 one of the 2-district pieces is discontiguous\n",
      "0 7 iii,try no. 2-district pops were 241726.0 244177.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg 0 481 one of the 2-district pieces is discontiguous\n",
      "0 8 iii,try no. 2-district pops were 243256.0 240130.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg 0 499 one of the 2-district pieces is discontiguous\n",
      "0 9 iii,try no. 2-district pops were 243888.0 238498.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg 0 517 one of the 2-district pieces is discontiguous\n",
      "0 10 iii,try no. 2-district pops were 241808.0 244048.0 vs tgts 243325 241177\n",
      "ABORT for iii,angle deg 0 535 one of the 2-district pieces is discontiguous\n",
      "total 4-district pops for vtd- and muni-snapped should be 486564.0 486596.0\n",
      "1 1 iii,try no. 2-district pops were 243543.0 245614.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 1 for triDistrict 1\n",
      "1 2 iii,try no. 2-district pops were 244204.0 242682.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 2 for triDistrict 1\n",
      "1 3 iii,try no. 2-district pops were 244084.0 241128.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 3 for triDistrict 1\n",
      "1 4 iii,try no. 2-district pops were 242860.0 245468.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 4 for triDistrict 1\n",
      "1 5 iii,try no. 2-district pops were 245036.0 243845.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 5 for triDistrict 1\n",
      "1 6 iii,try no. 2-district pops were 243646.0 245110.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 6 for triDistrict 1\n",
      "1 7 iii,try no. 2-district pops were 242964.0 245066.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 7 for triDistrict 1\n",
      "1 8 iii,try no. 2-district pops were 244319.0 243269.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 8 for triDistrict 1\n",
      "1 9 iii,try no. 2-district pops were 280542.0 241826.0 vs tgts 243282 243298\n",
      "ABORT for iii,angle deg= 1 430  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "1 10 iii,try no. 2-district pops were 242984.0 242321.0 vs tgts 243282 243298\n",
      "total 4-district pops for vtd- and muni-snapped should be 486565.0 486596.0\n",
      "2 1 iii,try no. 2-district pops were 242631.0 244860.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 1 for triDistrict 2\n",
      "2 2 iii,try no. 2-district pops were 243902.0 242829.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 2 for triDistrict 2\n",
      "2 3 iii,try no. 2-district pops were 243538.0 245361.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 3 for triDistrict 2\n",
      "2 4 iii,try no. 2-district pops were 244140.0 240937.0 vs tgts 243282 243298\n",
      "2 5 iii,try no. 2-district pops were 243634.0 241955.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 5 for triDistrict 2\n",
      "2 6 iii,try no. 2-district pops were 244287.0 241644.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 6 for triDistrict 2\n",
      "2 7 iii,try no. 2-district pops were 244490.0 241715.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 7 for triDistrict 2\n",
      "2 8 iii,try no. 2-district pops were 242385.0 241826.0 vs tgts 243282 243298\n",
      "2 9 iii,try no. 2-district pops were 242977.0 242346.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 9 for triDistrict 2\n",
      "2 10 iii,try no. 2-district pops were 242948.0 240920.0 vs tgts 243282 243298\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 10 for triDistrict 2\n",
      "total 4-district pops for vtd- and muni-snapped should be 485975.0 479745.0\n",
      "3 1 iii,try no. 2-district pops were 243441.0 239852.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 1 for triDistrict 3\n",
      "3 2 iii,try no. 2-district pops were 244240.0 236114.0 vs tgts 242987 239872\n",
      "ABORT for iii,angle deg 3 243 one of the 2-district pieces is discontiguous\n",
      "3 3 iii,try no. 2-district pops were 242348.0 236632.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 3 for triDistrict 3\n",
      "3 4 iii,try no. 2-district pops were 254030.0 243811.0 vs tgts 242987 239872\n",
      "ABORT for iii,angle deg= 3 279  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "3 5 iii,try no. 2-district pops were 242389.0 236927.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 5 for triDistrict 3\n",
      "3 6 iii,try no. 2-district pops were 242604.0 236852.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 6 for triDistrict 3\n",
      "3 7 iii,try no. 2-district pops were 242437.0 239995.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 7 for triDistrict 3\n",
      "3 8 iii,try no. 2-district pops were 243753.0 236253.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 8 for triDistrict 3\n",
      "3 9 iii,try no. 2-district pops were 242380.0 235505.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 9 for triDistrict 3\n",
      "3 10 iii,try no. 2-district pops were 242548.0 236710.0 vs tgts 242987 239872\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 10 for triDistrict 3\n",
      "total 4-district pops for vtd- and muni-snapped should be 486203.0 476633.0\n",
      "4 1 iii,try no. 2-district pops were 243294.0 236203.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 1 for triDistrict 4\n",
      "4 2 iii,try no. 2-district pops were 243112.0 236557.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 2 for triDistrict 4\n",
      "4 3 iii,try no. 2-district pops were 243996.0 234422.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 3 for triDistrict 4\n",
      "4 4 iii,try no. 2-district pops were 241575.0 237893.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 4 for triDistrict 4\n",
      "4 5 iii,try no. 2-district pops were 242612.0 241660.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 5 for triDistrict 4\n",
      "4 6 iii,try no. 2-district pops were 243175.0 233408.0 vs tgts 243101 238316\n",
      "ABORT for iii,angle deg 4 249 one of the 2-district pieces is discontiguous\n",
      "4 7 iii,try no. 2-district pops were 242478.0 232910.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 7 for triDistrict 4\n",
      "4 8 iii,try no. 2-district pops were 244806.0 240132.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 8 for triDistrict 4\n",
      "4 9 iii,try no. 2-district pops were 242303.0 236856.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 9 for triDistrict 4\n",
      "4 10 iii,try no. 2-district pops were 241435.0 233572.0 vs tgts 243101 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 10 for triDistrict 4\n",
      "total 4-district pops for vtd- and muni-snapped should be 485714.0 476633.0\n",
      "5 1 iii,try no. 2-district pops were 242874.0 237491.0 vs tgts 242857 238316\n",
      "ABORT for iii,angle deg 5 237 one of the 2-district pieces is discontiguous\n",
      "5 2 iii,try no. 2-district pops were 241951.0 241367.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 2 for triDistrict 5\n",
      "5 3 iii,try no. 2-district pops were 868.0 238709.0 vs tgts 242857 238316\n",
      "ABORT for iii,angle deg= 5 273  - our halfPops or halfPop3s are too far from 2*aDP\n",
      "5 4 iii,try no. 2-district pops were 242217.0 242127.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 4 for triDistrict 5\n",
      "5 5 iii,try no. 2-district pops were 241958.0 241575.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 5 for triDistrict 5\n",
      "5 6 iii,try no. 2-district pops were 243350.0 237099.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 6 for triDistrict 5\n",
      "5 7 iii,try no. 2-district pops were 242322.0 243981.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 7 for triDistrict 5\n",
      "5 8 iii,try no. 2-district pops were 242250.0 239878.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 8 for triDistrict 5\n",
      "5 9 iii,try no. 2-district pops were 243178.0 238339.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 9 for triDistrict 5\n",
      "5 10 iii,try no. 2-district pops were 242473.0 237933.0 vs tgts 242857 238316\n",
      "5036 has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try 10 for triDistrict 5\n",
      "we drew a TOTAL of 41 legit 4-district Summit sets out of 10 x 6\n"
     ]
    }
   ],
   "source": [
    "#Next for Summit (option B)- chop into 4 districts.  Duplicate the optionA Summit code\n",
    "print(\"A: the below builds Summit 4-district sets for each Summit triArea.  10 tries / triArea, we store any legit 4-district set\")\n",
    "toler = 0.05 * aDP\n",
    "Summ4BLISTS, Summ4BLISTS3, Summ4BdiNo = list(), list(), list()  #\"B\" to distinguish from option A above.  \"diNo\" refers to Cuya-Summ dilist no\n",
    "\n",
    "c = ppC #Summit from above block\n",
    "for iii, L in enumerate(CuyaSummDiLists):\n",
    "    ppCpool, ppCpool3, muni2dPool3, ppCpoolPop, ppCpoolPop3 = list(), list(), list(), 0., 0.\n",
    "    for v in countyTractList[c] :\n",
    "        if v not in L:\n",
    "            ppCpool.append(v)\n",
    "            ppCpoolPop += tractPop3[v]\n",
    "        if v not in CuyaSummDiLists3[iii]:\n",
    "            ppCpool3.append(v)\n",
    "            ppCpoolPop3 += tractPop3[v]\n",
    "            if tractMuniNo[v] not in muni2dPool3:  #remember, none of the CuyaSumm di-districts had a Summit split\n",
    "                muni2dPool3.append(tractMuniNo[v])  #the munis not in the straddler HD3 - used later in tryNoSplit 2district\n",
    "                \n",
    "    tgt2Pop =  2. * (ppCpoolPop )  / int(countyDistricts[ppC])  #\"2.\" for 1st of two orthog pizzacuts\n",
    "    tgt2Pop3 = 2. * (ppCpoolPop3) / int(countyDistricts[ppC])\n",
    "    maxPop, minPop = 2.05*aDP, 1.95*aDP #narrowing these a bit to increase success of later splitting 2 --> 4\n",
    "    tol2 = r3(max(0.02*aDP,min(maxPop - tgt2Pop , tgt2Pop - minPop)) ) #for simplicity, narrow the range so we won't fail on either side ...\n",
    "    tol3 = r3(max(0.02*aDP, min(maxPop - tgt2Pop3, tgt2Pop3- minPop)) ) # ... but force a positive small tolerance\n",
    "    #print(\"for vtdNo\",t,\"targetPops,(tol) for 2-districts w vtdSnap, muniSnap are\",int(tgt2Pop),\"(\",tol2, int(tgt2Pop3),\"(\",tol3,\")\" )\n",
    "    #print(\" ... based on countyPop, snappedHDpop, HD3pop of\", countyPop[mC], mCsnapPop, mCsnapPop3)\n",
    "\n",
    "    print(\"total 4-district pops for vtd- and muni-snapped should be\",ppCpoolPop, ppCpoolPop3)\n",
    "\n",
    "    cutPoint = get_PCP(ppCpool, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts   \n",
    "    cutPoint3 = get_PCP(ppCpool3, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts       \n",
    "    drawUnsuccessful = True\n",
    "    maxTries = 10 #10 #5 #15\n",
    "    nTries = 0\n",
    "    nInPlayTracts = len(ppCpool)\n",
    "    sliceAngle = random.uniform(0., 2.*pi)\n",
    "    while nTries < maxTries: #and drawUnsuccessful == True:  #create the four districts triggered by a random initial cut angle ...\n",
    "        drawUnsuccessful = True #reset here b/c we take any legit 4-district set for each Summit triArea partial\n",
    "        tempLISTS, tempLISTS3 = list(), list() #these will house the 4 district tractLists when both vtd- and muni-drawn 4 are Jiggy\n",
    "        halfList, halfList3, halfPop, halfPop3 = [ [],[] ], [ [],[] ], [0.,0.], [0.,0.]\n",
    "        soFarSoGood = True\n",
    "        nTries +=1  #3/7/23 modification - do NOT specify a starter tract, let cutVTDsnap figure it out\n",
    "        needStarterTract = True\n",
    "        #while needStarterTract:\n",
    "        #    mCcT = mCpool[random.randint(nInPlayTracts)] #... based on a random vtd in these 4 districts for BOTH snapping technqs\n",
    "        #    if mCcT in mCpool3:\n",
    "        #        needStarterTract = False\n",
    "        sliceAngle += math.pi / maxTries  #we methodically try a series of possible cut angles.  (Some will lead to discontinuity)\n",
    "        sliceAngle3 = sliceAngle\n",
    "        # = getSliceAngle(tractCP[mCcT], cutPoint, tractCP[mCcT])  #we use same angle for vtd- & muni-snapped\n",
    "        halfList[0], halfPop[0] = cutVtdSnap(tgt2Pop, tol2, tractCP, ppCpool, neighborList, tractPop3, sliceAngle, cutPoint)\n",
    "            \n",
    "        halfList3[0], hML, halfPop3[0], splitMuni = cutMuniSnap(tgt2Pop3, tol3, muniTractList,tractCP, ppCpool3, muniNeighborList, \n",
    "                                                                tractPop3, muniPop, muniPCP, sliceAngle, cutPoint3)\n",
    "        popGap, fillPop = tgt2Pop3 - halfPop3[0], 0.\n",
    "        if abs(popGap) > tol3 :  #try alternate muni's, do not allow a split at this stage\n",
    "            #print(t,nTries,len(halfList3[0]),halfPop3[0], popGap, int(tol3),\"t,nTries, halfList3[0] len, pop & its popGap, tol3 prior to gFL3nS attempt\")\n",
    "            hT = getFarthestTract(cutPoint3, halfList3[0], tractCP)  #\"home\" tract to bias fill-in toward compactness\n",
    "            vlist, fillPop = getFillList3noSplit(hT, popGap, tol3,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,hML,\n",
    "                                             muni2dPool3, tractCP, neighborList, tractPop3)\n",
    "            halfList3[0] += vlist\n",
    "            halfPop3[0]  += fillPop\n",
    "            #print(\"gFL3nS added pop of\",fillPop,\"from\",len(vlist),\"vtds.  halfList3 len now\",len(halfList3[0]) )\n",
    "        \n",
    "        print(iii,nTries,\"iii,try no. 2-district pops were\",halfPop[0],halfPop3[0],\"vs tgts\",int(tgt2Pop), int(tgt2Pop3)) \n",
    "        for v in ppCpool:\n",
    "            if v not in halfList[0]:\n",
    "                halfList[1].append(v)\n",
    "                halfPop[1]  += tractPop3[v]\n",
    "        muniPool3 = [list(), list()]  #the list of munis in each half of the 4-district set (we did not allow splitMuni in prev cut)\n",
    "        for v in ppCpool3:\n",
    "            if v not in halfList3[0]:\n",
    "                halfList3[1].append(v)\n",
    "                halfPop3[1] += tractPop3[v]\n",
    "            elif tractMuniNo[v] not in muniPool3[0]:\n",
    "                muniPool3[0].append(tractMuniNo[v])\n",
    "        for m in countyMuniList[ppC]:\n",
    "            if m not in muniPool3[0] and muniTractList[m][0] not in CuyaSummDiLists3[iii]:\n",
    "                muniPool3[1].append(m)\n",
    "        #print(\"and other-slice halfpops were\",halfPop[1], halfPop3[1])\n",
    "        #sIC = 0\n",
    "        #for v in countyTractList[mC]:\n",
    "        #    if v in HDtractList3[t]:\n",
    "        #        sIC +=1\n",
    "        #print(\"nVTDs in half-munisnap0, half-munisnap1, straddler-in-county, total county are\", len(halfList3[0]), len(halfList3[1]),sIC,len(countyTractList[mC]))\n",
    "        for v in countyTractList[ppC]:\n",
    "            if v in halfList3[0] and v in halfList3[1]:\n",
    "                raise Exception(v,\"is in both halfLists for\",iii,nTries)\n",
    "            if v in halfList3[1] and v in CuyaSummDiLists3[iii]:\n",
    "                raise Exception(v,\"is in 2nd HL3 and straddler for\",iii,nTries)\n",
    "            if v in halfList3[0] and v in CuyaSummDiLists3[iii]:\n",
    "                raise Exception(v,\"is in 1st HL3 and straddler for\",iii,nTries)\n",
    "        \n",
    "        if not ( isContiguous(halfList[1],neighborList) and isContiguous(halfList3[1],neighborList) ) :\n",
    "            soFarSoGood = False\n",
    "            print(\"ABORT for iii,angle deg\",iii,int(180./3.1416*sliceAngle),\"one of the 2-district pieces is discontiguous\")\n",
    "            continue  #try a new angle; early discontiguity\n",
    "        if max(abs(halfPop[0]-2*aDP),abs(halfPop[1]-2*aDP),abs(halfPop3[0]-2*aDP), abs(halfPop3[1]-2*aDP) ) > 0.08 * aDP:\n",
    "            soFarSoGood = False\n",
    "            print(\"ABORT for iii,angle deg=\",iii, int(180./3.1416*sliceAngle),\" - our halfPops or halfPop3s are too far from 2*aDP\")\n",
    "            continue #try a new angle; unlikely to hit pop range for qtr districts\n",
    "            \n",
    "        #OK, both 2-district sets are good.  Now try some angles for splitting each of these into \"qtr\" districts\n",
    "        qtrList, qtrList3, qtrPop, qtrPop3 = [list(),list(),list(),list()], [list(),list(),list(),list()], [0.]*4, [0.]*4\n",
    "        nGoodQtrCuts = 0\n",
    "        vtdAngle = [0.]*2\n",
    "        for i, HL in enumerate(halfList):  #first, attempt to generate four good qtr districts for vtd-snap\n",
    "            off2Pop = abs(0.5*halfPop[i] - aDP)\n",
    "            TOL = max( 0.02*aDP, 0.05*aDP - 0.5*off2Pop)  #tighten if 2-district off\n",
    "            qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            qtrCutNo = random.randint(maxQtrCutNo) #we'll make four 45deg-spaced cut tries, but randomize which to try first\n",
    "            \n",
    "            while qLoopNo < maxQtrCutNo: #and haveCutGoodQtr; make all 4 possiblel cuts, keep trying for better\n",
    "                qAngle = sliceAngle + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo #0deg, 45deg, 90deg, 135deg vs orig cut\n",
    "                qElist, qtrPop[qE] = cutVtdSnap(0.5*halfPop[i], TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)  \n",
    "                qtrPop[qO] = halfPop[i] - qtrPop[qE]\n",
    "                qOlist = list()\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop[qE] - aDP) <= 0.05*aDP and abs(qtrPop[qO] - aDP) <= 0.05*aDP :  \n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList[qE], qtrList[qO] = qElist.copy(), qOlist.copy()\n",
    "                            vtdAngle[i] = qAngle #to preserve same angle for 1st try in later munisnap 4-district set\n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        if nGoodQtrCuts < 2:  #we failed to make four contiguous districts for vtd-snap case.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty vtd-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            #plotPoly(countyGeom[mC])\n",
    "            #plotPoly(stradPoly)\n",
    "            #for q in qtrList:\n",
    "            #    if not isContiguous(q,neighborList):\n",
    "            #        for v in q:\n",
    "            #            plotPoly(tractGeom[v])\n",
    "            #plt.show()\n",
    "            continue  #don't bother quartering up the muni-snap\n",
    "            \n",
    "        for i, HL in enumerate(halfList3):  #... and now, 4 qtr-districts for muni-snap\n",
    "            qCutPoint3  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            off2Pop3 = abs(0.5*halfPop3[i] - aDP)\n",
    "            TOL3 = max( 0.02 * aDP, toler - 0.5*off2Pop3)  #tighten if 2-district off\n",
    "            qtrCutNo ,maxQtrCutNo, cutGoodQtr = 0, 4, False\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            qLoopNo = 0\n",
    "            sliceAngle3 = vtdAngle[i]  #of four try angles, we start at the angle that worked for vtdSnap \n",
    "            while qLoopNo < maxQtrCutNo : #and not cutGoodQtr :\n",
    "                qAngle = sliceAngle3 + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo               \n",
    "                #Polly = angledPentaPoly(cutPoint3, qAngle, 5.*tractCP[mCfT].distance(cutPoint3) )\n",
    "                #if Polly.disjoint(tractCP[mCfT]):\n",
    "                #    qAngle += math.pi\n",
    "                qElist, qML, qtrPop3[qE], splitMuni = cutMuniSnap(0.5*halfPop3[i], TOL3, muniTractList,tractCP, HL,\n",
    "                                                                      muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "                \n",
    "                popGap = 0.5*halfPop3[i] - qtrPop3[qE]\n",
    "                list3B, pop3B = list(),0.\n",
    "                if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "                    hT = getFarthestTract(cutPoint3, qElist, tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "                    list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,qML,\n",
    "                                                              muniPool3[i],tractCP, neighborList,tractPop3,0)\n",
    "                qElist, qtrPop3[qE] = qElist + list3B, qtrPop3[qE] + pop3B\n",
    "                qtrPop3[qO] = halfPop3[i] - qtrPop3[qE]\n",
    "                qOlist = list()\n",
    "                #print(\"t,i\",t,i, qtrPop[qE],qtrPop[qO],\"<-qpops, q3pops -->\",qtrPop3[qE],qtrPop3[qO])  #debug\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop3[qE] - aDP) <= 0.05*aDP and abs(qtrPop3[qO] - aDP) <= 0.05*aDP :\n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList3[qE], qtrList3[qO] = qElist.copy(), qOlist.copy()               \n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        #print(\"t,tryNo, final dPops for vtd- and muni\",t,nTries,qtrPop,qtrPop3)\n",
    "        if nGoodQtrCuts < 4:  #we failed to make four contiguous districts for both cases.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty muni-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            continue\n",
    "        \n",
    "        if soFarSoGood:\n",
    "            drawUnsuccessful = False\n",
    "            print(t,\"has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try\",nTries,\"for triDistrict\",iii)\n",
    "            for qD in range(4):\n",
    "                tempLISTS.append(qtrList[qD])\n",
    "                tempLISTS3.append(qtrList3[qD])\n",
    "            Summ4BLISTS.append(tempLISTS)\n",
    "            Summ4BLISTS3.append(tempLISTS3)\n",
    "            Summ4BdiNo.append(iii)  #the iii points back to an index in the list of CuyaSumm districts\n",
    "print(\"we drew a TOTAL of\",len(Summ4BLISTS),\"legit 4-district Summit sets out of\",maxTries,\"x\",len(CuyaSummDiLists) )\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "83ee3583-64eb-4369-971d-662c7056eb3d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the Summit 4B whole-district lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the Summit 4B whole-district lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "date = \"01Apr\"\n",
    "rLISTS = [Summ4BLISTS.copy(), Summ4BLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "pointerList = Summ4BdiNo.copy()\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    triPointer = list() \n",
    "    #dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    planNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            #dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            planNo.append(p)\n",
    "            triPointer.append(pointerList[p])    \n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"planNo\":planNo,\"triCounty pointer\":triPointer,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"Summ4B\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "491e07c7-60cb-4c04-a4f7-9e64ff98edd6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "continuing option B. Cuya-Summ done, now the rest (77-3, 3-27, 27-66-75 (with 27 cut along SW-NE diag or E-W)\n",
      "try different 75 Stark NE corners via swelling, pair w E. Portage 66 and Geauga\n",
      "we will add 7241.0 from county 14 to boost the avg NE district pop from 7.816 of aDP to 7.866 of aDP\n",
      "our true helpPop for both vtd- and muni-snap from county 14 will be 4068.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"continuing option B. Cuya-Summ done, now the rest (77-3, 3-27, 27-66-75 (with 27 cut along SW-NE diag or E-W)\")\n",
    "print(\"try different 75 Stark NE corners via swelling, pair w E. Portage 66 and Geauga\")\n",
    "mC, ppC, tpC = 75,66,27 #Stark, Portage, Geauga\n",
    "NElistB = [75,66,27,3,77]\n",
    "NElistBpop = 0.\n",
    "for c in NElistB:\n",
    "    NElistBpop += countyPop[c]\n",
    "NEdist = r3(NElistBpop/aDP)\n",
    "helpC = 14\n",
    "helpPopTgt = 0.05 * aDP\n",
    "NEdistB = r3((NElistBpop + helpPopTgt)/aDP)\n",
    "print(\"we will add\",helpPop,\"from county\",helpC,\"to boost the avg NE district pop from\",NEdist,\"of aDP to\",NEdistB,\"of aDP\")\n",
    "helpStarter = getClosestTract(tractCP[7839],countyTractList[helpC],tractCP)\n",
    "helpTracts, helpPop, xx =  muniSwell(tractCP[helpStarter], 1.05*helpPopTgt, 0.02*aDP, aDP, muniPop, muniPCP, muniTractList, \n",
    "                                    muniNeighborList,countyMuniList[helpC], tractCP, neighborList, tractPop3, 0, 1.)\n",
    "print(\"our true helpPop for both vtd- and muni-snap from county\",helpC,\"will be\",helpPop)\n",
    "for v in helpTracts:\n",
    "    plotPoly(tractGeom[v])\n",
    "\n",
    "for v in countyTractList[mC]:\n",
    "    for nV in neighborList[v]:\n",
    "        if nV in countyTractList[ppC]:\n",
    "            plotPoly(tractGeom[v])\n",
    "            plotCenter(v,tractGeom[v])\n",
    "            break\n",
    "plotPoly(countyGeom[mC])\n",
    "plotPoly(countyGeom[ppC])\n",
    "for m in countyMuniList[mC]:\n",
    "    if muniGeom[m].intersects(countyGeom[ppC]):\n",
    "        plotPoly(muniGeom[m])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "1d2d655f-2070-436b-9b15-ae8b28635a33",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for m in countyMuniList[75]:\n",
    "    if muniGeom[m].distance(countyGeom[66]) < 0.1:\n",
    "        plotPoly(muniGeom[m])\n",
    "        plotCenter(muniPop[m],muniGeom[m])\n",
    "        plt.text(muniPCP[m].x,muniPCP[m].y-0.01,m,fontsize=6,ha='center')\n",
    "plotPoly(countyGeom[75])\n",
    "plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "33e1e0b7-59f3-4e85-8e01-f06b765ba6c1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing the Geauga vtd's that intersect Portage -- likely in optionB Stark-Port-Geau triDistrict\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing the Geauga vtd's that intersect Portage -- likely in optionB Stark-Port-Geau triDistrict\")\n",
    "tpC = 27\n",
    "for v in countyTractList[tpC]:\n",
    "    if tractGeom[v].intersects(countyGeom[66]):\n",
    "        plotPoly(tractGeom[v])\n",
    "        plotCenter(v, tractGeom[v])\n",
    "plotPoly(countyGeom[tpC])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6b942412-11a1-4357-b56e-aa84fbd1b707",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there is really only 1 Stark corner option for east Portage pairing, and it doesn't intersect helpC\n",
      "Now build the StarkPortGeau triCounty district\n",
      "The tgtPop for Stark-Port-Geau will be 115610 vs usual 119186\n",
      "by county, these targets are 26547 46180 42883\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 251.999 108.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 251.999 135.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 251.999 162.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 251.999 189.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 251.999 215.999\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 263.999 108.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 263.999 135.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 263.999 162.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 263.999 189.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 263.999 215.999\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 275.999 108.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 275.999 135.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 275.999 162.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 275.999 189.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 275.999 215.999\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 287.999 108.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 287.999 135.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 287.999 162.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 287.999 189.0\n",
      "GOOD StarkPortGea for starter,PortAngle,GeaAngle 7839 287.999 215.999\n",
      "All done creating Stark-Portage-Geauga straddlers.  We had 20 successes out of 20\n"
     ]
    }
   ],
   "source": [
    "print(\"there is really only 1 Stark corner option for east Portage pairing, and it doesn't intersect helpC\")\n",
    "print(\"Now build the StarkPortGeau triCounty district\")\n",
    "debug = False\n",
    "swellStarters = [7839]  #,7849]  #Stark portion is tiny, so only create one\n",
    "StarkTriForcedMunis = [1714, 1844]  #KISS - these two Stark muni's are in every triCounty straddler.  Otherwise, Alliance is boxed out\n",
    "StarkPortGeau = [75,66,27]\n",
    "mC, ppC, tpC = [75,66,27]\n",
    "#cutAngle = [1.15*math.pi, 1.2*math.pi]\n",
    "wholeDistrictShadeRatio = 0.97  #need to shade down because the 5-county pop is 7.82, not 8.00\n",
    "StarkBoost = 0. #0.06  #inflate the Stark straddler pop to help Port-Gea-Asht-Trum districts hit target\n",
    "forcedStarkPop = 0.\n",
    "for m in StarkTriForcedMunis:\n",
    "    forcedStarkPop += muniPop[m]\n",
    "tgtMCpop =  forcedStarkPop #countyPop[mC]%aDP + StarkBoost*aDP\n",
    "RaDP = wholeDistrictShadeRatio * aDP\n",
    "tgtPPCpop = countyPop[ppC]%RaDP\n",
    "tgtTPCpop = RaDP - tgtPPCpop - tgtMCpop\n",
    "tgtPop =  tgtMCpop + tgtPPCpop + tgtTPCpop\n",
    "triToler = 0.8 * min(tgtPop - 0.95*aDP , 1.05*aDP*tgtPop)  #tighten some since we are adding three partials\n",
    "print(\"The tgtPop for Stark-Port-Geau will be\",int(tgtPop),\"vs usual\",int(aDP))\n",
    "print(\"by county, these targets are\",int(tgtMCpop), int(tgtPPCpop), int(tgtTPCpop))\n",
    "toler = 0.05*aDP\n",
    "SPGtriLists, SPGtriLists3, SPGstarterLists = list(), list(), list()  #SPGstarter is the index of the Stark swell starter (only 1 for now)\n",
    "for iii, t in enumerate(swellStarters):\n",
    "    homeM = tractMuniNo[t]\n",
    "    cTcP = tractCP[t]\n",
    "    mClist, mClist3, mCpop, mCpop3 = list(), list(), 0., 0.\n",
    "    for m in StarkTriForcedMunis:\n",
    "        mClist  += muniTractList[m]\n",
    "        mClist3 += muniTractList[m]\n",
    "        mCpop  += muniPop[m]\n",
    "        mCpop3 += muniPop[m]\n",
    "    #mClist, mCpop = vtdSwell(cTcP, tgtMCpop, triToler, tractCP, countyTractList[mC],neighborList,tractPop3,1.)   \n",
    "    #mClist3, mCpop3, totSplits = mClist.copy(), mCpop, 0\n",
    "    #mClist3, mCpop3, totSplits = muniSwell(cTcP, tgtMCpop, triToler, aDP, muniPop, muniPCP, muniTractList,\n",
    "    #                                       muniNeighborList,countyMuniList[mC], tractCP, neighborList, tractPop3, 1, 1.) #don't allow split here\n",
    "    \n",
    "    Port_hTcP = tractCP[ getClosestTract(tractCP[t], countyTractList[ppC],tractCP) ]  #Portage home tract centerpoint, closest to Stark starter\n",
    "    minPortAngle, maxPortAngle, nPortAngles = 0.4*math.pi, 0.6*math.pi,4\n",
    "    for angleNo in range(nPortAngles):\n",
    "        angle = minPortAngle + (maxPortAngle - minPortAngle)*float(angleNo)/(nPortAngles-1.0)\n",
    "        if angledPentaPoly(countyPCP[ppC],angle, 5*countyGeom[ppC].area**0.5 ).disjoint(Port_hTcP):\n",
    "            angle += math.pi\n",
    "        cutPoint = countyPCP[ppC]\n",
    "        ppClist, ppCpop = cutVtdSnap(tgtPPCpop, triToler, tractCP, countyTractList[ppC], neighborList,tractPop3, angle, cutPoint)\n",
    "        ppClist3, pML, ppCpop3, sM = cutMuniSnap(tgtPPCpop, triToler, muniTractList,tractCP, countyTractList[ppC],\n",
    "                                                 muniNeighborList,tractPop3, muniPop, muniPCP, angle, cutPoint)\n",
    "        popGap, fillPop = tgtPPCpop - ppCpop3, 0.\n",
    "        if abs(popGap) > triToler :  #try alternate muni's, do not allow a split at this stage\n",
    "            hT = getFarthestTract(cutPoint, ppClist3, tractCP)  #\"home\" tract to bias fill-in toward compactness\n",
    "            vlist, fillPop = getFillList3noSplit(hT, popGap, triToler,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,pML,\n",
    "                                                 countyMuniList[ppC], tractCP, neighborList, tractPop3)\n",
    "            ppClist3 += vlist\n",
    "            ppCpop3  += fillPop\n",
    "        \n",
    "        Gea_hTcP = tractCP[2443] #see above block for forcing this southern Geauga tract into the triCounty district\n",
    "        minGeaAngle, maxGeaAngle, nGeaAngles = -0.4*math.pi, 0.2*math.pi,5\n",
    "        tToler = min(tgtTPCpop - 0.95*aDP , 1.05*aDP*tgtMCpop)\n",
    "        for gAngleNo in range(nGeaAngles):\n",
    "            gAngle = minGeaAngle + (maxGeaAngle - minGeaAngle)*float(gAngleNo)/(nGeaAngles-1.0)\n",
    "            if angledPentaPoly(countyPCP[tpC],gAngle, 5*countyGeom[tpC].area**0.5 ).disjoint(Gea_hTcP):\n",
    "                gAngle += math.pi\n",
    "            cutPoint = countyPCP[tpC]\n",
    "            tpClist, tpCpop = cutVtdSnap(tgtTPCpop, triToler, tractCP, countyTractList[tpC], neighborList,tractPop3, gAngle, cutPoint)\n",
    "            tpClist3, tML, tpCpop3, sM = cutMuniSnap(tgtTPCpop, triToler, muniTractList, tractCP, countyTractList[tpC],\n",
    "                                                     muniNeighborList,tractPop3, muniPop, muniPCP, gAngle, cutPoint)\n",
    "            popGap, fillPop = tgtTPCpop - tpCpop3, 0.\n",
    "            if abs(popGap) > triToler :  #try alternate muni's, do not allow a split at this stage\n",
    "                hT = getFarthestTract(cutPoint, tpClist3, tractCP)  #\"home\" tract to bias fill-in toward compactness\n",
    "                vlist, fillPop = getFillList3noSplit(hT, popGap, triToler,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,tML,\n",
    "                                                     countyMuniList[tpC], tractCP, neighborList, tractPop3)\n",
    "                tpClist3 += vlist\n",
    "                tpCpop3  += fillPop\n",
    "                \n",
    "            stradList, stradList3 = mClist + ppClist + tpClist, mClist3 + ppClist3 + tpClist3\n",
    "            stradPop, stradPop3   = mCpop + ppCpop + tpCpop,    mCpop3 + ppCpop3 + tpCpop3\n",
    "            if isContiguous(stradList, neighborList) and isContiguous(stradList3, neighborList):\n",
    "                if abs(stradPop - aDP) < 0.05 * aDP and abs(stradPop3 - aDP) < 0.05 * aDP :  #and totSplits <= 1:\n",
    "                    SPGtriLists.append(stradList)\n",
    "                    SPGtriLists3.append(stradList3)\n",
    "                    SPGstarterLists.append(iii)\n",
    "                    print(\"GOOD StarkPortGea for starter,PortAngle,GeaAngle\",t,r3(180/3.1416*angle),r3(180/3.1416*gAngle))\n",
    "                else:\n",
    "                    print(\"pop FAIL for StarkPortGea for starter,PortAngle,GeaAngle\",t,r3(180/3.1416*angle),r3(180/3.1416*gAngle))\n",
    "                    print(mCpop3, ppCpop3, tpCpop3, stradPop3)\n",
    "            else:\n",
    "                print(\"contig FAIL for StarkPortGea for starter,PortAngle,GeaAngle\",t,r3(180/3.1416*angle),r3(180/3.1416*gAngle))\n",
    "                for v in stradList3:\n",
    "                    plotPoly(tractGeom[v])\n",
    "                plotPoly(countyGeom[mC])\n",
    "                plotPoly(countyGeom[ppC])\n",
    "                plotPoly(countyGeom[tpC])\n",
    "                plt.show()\n",
    "print(\"All done creating Stark-Portage-Geauga straddlers.  We had\",len(SPGtriLists),\n",
    "      \"successes out of\",len(swellStarters)*nPortAngles*nGeaAngles )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "476ae381-5f90-4943-8275-153dcf0d9da2",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the StarkPortGea lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the StarkPortGea B lists to a file.  We will vote later\" ) \n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo   \n",
    "date = \"03Apr\"\n",
    "rLISTS = [SPGtriLists.copy(), SPGtriLists3.copy()] \n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo = list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"StarkPortGeaB\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "662bb6fe-e710-48b5-9fc4-e3baf148708d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i,L in enumerate(SPGtriLists):\n",
    "    pop = 0.\n",
    "    for v in L:\n",
    "        pop += tractPop3[v]\n",
    "    plt.scatter(i,pop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "dccf9715-6412-4c6f-b82a-12292224af6f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "visualizing one of the successful muni-snapped Stark-Portage-Geauga straddlers\n",
      "14\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"visualizing one of the successful muni-snapped Stark-Portage-Geauga straddlers\")\n",
    "j = random.randint(len(SPGtriLists3))\n",
    "for v in SPGtriLists3[j]:\n",
    "    plotPoly(tractGeom[v])\n",
    "plotPoly(countyGeom[ppC])\n",
    "print(j)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "391aca0f-0a70-4d1c-bb41-ffa373ffe475",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n"
     ]
    }
   ],
   "source": [
    "print(len(SPGtriLists))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e9b3af0f-10f7-4c95-906d-ee7117d56125",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now, create Geauga-Asht and Asht-Trum options for each of these schemeB triCounty districts\n",
      "Modify schemeA's GAT code for this\n",
      "0 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117244 117244 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 0 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "1 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117526 117526 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 1 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "2 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117404 117404 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 2 . nGood, nFailed sets = 54.0 6.0\n",
      "total should be 6  x  10\n",
      "3 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117117 117117 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 3 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "4 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117496 117496 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 4 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "5 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117244 117244 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 5 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "6 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117526 117526 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 6 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "7 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117404 117404 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 7 . nGood, nFailed sets = 54.0 6.0\n",
      "total should be 6  x  10\n",
      "8 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117117 117117 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 8 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "9 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117496 117496 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 9 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "10 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117244 117244 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 10 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "11 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117526 117526 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 11 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "12 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117404 117404 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 12 . nGood, nFailed sets = 54.0 6.0\n",
      "total should be 6  x  10\n",
      "13 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117117 117117 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 13 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "14 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117496 117496 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 14 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "15 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117244 117244 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 15 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "16 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117526 117526 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 16 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "17 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117404 117404 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 17 . nGood, nFailed sets = 54.0 6.0\n",
      "total should be 6  x  10\n",
      "18 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117117 117117 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 18 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n",
      "19 triggerNo. For the 3.0 remaining GeaAshTrum districts, our target pops are 117496 117496 vs usual 119186.34343434343\n",
      "all done making GeaAshTrum sets for StarkPortGea starter no 19 . nGood, nFailed sets = 58.0 2.0\n",
      "total should be 6  x  10\n"
     ]
    }
   ],
   "source": [
    "print(\"Now, create Geauga-Asht and Asht-Trum options for each of these schemeB triCounty districts\")\n",
    "print(\"Modify schemeA's GAT code for this\")\n",
    "GeaAshTrumBLISTS, GeaAshTrumBLISTS3, GeaAshTrumBTriggers = list(), list(), list()\n",
    "qpC, pC5 = 3, 77 #quarternary pairing county = Ashtabula.  5th paired county = Trumbull.  tpC already defined as Geauga\n",
    "nAshCuts, nTrumCuts = 6,10\n",
    "toler=0.05*aDP\n",
    "nFail, nGood = [ 0. for L in SPGtriLists], [ 0. for L in SPGtriLists]\n",
    "for jj, L in enumerate(SPGtriLists):\n",
    "    tpCremnList, tpCremnList3 = list(), list()  #Geauga vtd's not drawn into the straddler\n",
    "    tpCremnPop, tpCremnPop3 = 0., 0.\n",
    "    for v in countyTractList[tpC]:  #assign these remaining Geauga vtd's to the next district\n",
    "        if v not in L:\n",
    "            tpCremnList.append(v)\n",
    "            tpCremnPop += tractPop3[v]\n",
    "        if v not in SPGtriLists3[jj]:\n",
    "            tpCremnList3.append(v)\n",
    "            tpCremnPop3 += tractPop3[v]\n",
    "    regionalPop = countyPop[qpC] + countyPop[pC5] + tpCremnPop\n",
    "    regionalPop3= countyPop[qpC] + countyPop[pC5] + tpCremnPop\n",
    "    RaDP, RaDP3 = regionalPop / round(regionalPop/aDP,0) , regionalPop3 / round(regionalPop3/aDP)\n",
    "    print(jj,\"triggerNo. For the\",round(regionalPop/aDP,0),\"remaining GeaAshTrum districts, our target pops are\",int(RaDP), int(RaDP3),\"vs usual\",aDP)\n",
    "    tolr = min(min(RaDP, RaDP3) - 0.95*aDP , 1.05*aDP - max(RaDP, RaDP3) )\n",
    "    qpCtgtPop, qpCtgtPop3 = RaDP - tpCremnPop, RaDP3 - tpCremnPop3\n",
    "    qpCcT = getClosestTract(Point(-81., 41.8),countyTractList[qpC],tractCP)  #force NW Ashtabula tract into Gea-Asht, not Gea-Trum\n",
    "    qAngles = [math.pi* (0.25+ (0.65-0.25)*float(n+0.5)/nAshCuts) for n in range(nAshCuts)]  #NE - N - NW cutting angles for Asht\n",
    "    for qpCangle in qAngles:\n",
    "        if angledPentaPoly(countyPCP[qpC],qpCangle,5.*countyGeom[qpC].area).disjoint(tractCP[qpCcT]):\n",
    "            qpCangle += math.pi #oops, need complementary angle to capture\n",
    "        qpClist, qpCsnapPop = cutVtdSnap(qpCtgtPop, tolr, tractCP, countyTractList[qpC],neighborList,tractPop3,\n",
    "                                        qpCangle, countyPCP[qpC],qpCcT) \n",
    "        qpClist3, qpcML3, qpCsnapPop3, splitMuni = cutMuniSnap(qpCtgtPop3, tolr, muniTractList, tractCP, countyTractList[qpC],\n",
    "                                                             muniNeighborList,tractPop3, muniPop, muniPCP, qpCangle, countyPCP[qpC])  \n",
    "        popGap = qpCtgtPop3 - qpCsnapPop3\n",
    "        if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "            if popGap < 0  :\n",
    "                print(\"munisnap pop of\",spCsnapPop3,\"vs tgt\",spCtgtPop3,\"and\",ppCsnapPop3,\"in cty\",ppC)\n",
    "                raise Exception(\"error! spCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            qpClist3B, qpCsnapPop3B, nSplits = tryNoSplit(t, popGap, tolr, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,qpcML3,\n",
    "                                                        countyMuniList[qpC],tractCP, neighborList,tractPop3,0)        \n",
    "            qpClist3, qpCsnapPop3 = qpClist3 + qpClist3B, qpCsnapPop3 + qpCsnapPop3B\n",
    "        tpCqpClist, tpCqpClist3 = tpCremnList + qpClist,   tpCremnList3 + qpClist3\n",
    "        tpCqpCPop, tpCqpCPop3   = tpCremnPop + qpCsnapPop, tpCremnPop3 + qpCsnapPop3\n",
    "        isJiggy = True\n",
    "        for i, pair in enumerate([ [tpCqpClist, tpCqpCPop], [tpCqpClist3, tpCqpCPop3] ]):\n",
    "            if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < toler):\n",
    "                isJiggy = False\n",
    "                print(\"darn, we made a bad tpCqpC district\",i,\"(0 for vtd snap, 1 for muni) with pop\",pair[1])\n",
    "                for v in pair[0]:\n",
    "                    plotPoly(tractGeom[v])\n",
    "                plotPoly(countyGeom[ppC])\n",
    "                plotPoly(countyGeom[spC])\n",
    "                plt.show()\n",
    "        if not isJiggy:\n",
    "            nFail[jj] += nTrumCuts #we didn't get far enough to slice Trumbull, so all would have failed\n",
    "        if isJiggy:  #proceed to variations on an Asht-Trum and full Trumbull district to close out the set\n",
    "            #print(\"here was your ppC-spC district for muni-snapped\")\n",
    "            #for v in ppCspClist3:\n",
    "            #    plotPoly(tractGeom[v])\n",
    "            #plotPoly(countyGeom[spC])\n",
    "            #plt.show()\n",
    "            #pause = input(\"OK?\")\n",
    "            qpCremnList, qpCremnList3 = list(), list()  #Geauga vtd's not drawn into the straddler\n",
    "            qpCremnPop, qpCremnPop3 = 0., 0.\n",
    "            for v in countyTractList[qpC]:  #assign these remaining Ashtabula vtd's to the next district\n",
    "                if v not in tpCqpClist:\n",
    "                    qpCremnList.append(v)\n",
    "                    qpCremnPop += tractPop3[v]\n",
    "                if v not in tpCqpClist3:\n",
    "                    qpCremnList3.append(v)\n",
    "                    qpCremnPop3 += tractPop3[v]\n",
    "            RRaDP, RRaDP3 = 0.5*( countyPop[pC5]+qpCremnPop ), 0.5*( countyPop[pC5]+qpCremnPop3 )\n",
    "            tolr5 = min(min(RRaDP, RRaDP3) - 0.95*aDP , 1.05*aDP - max(RRaDP, RRaDP3) )\n",
    "            pC5tgtPop, pC5tgtPop3 = RRaDP - qpCremnPop, RRaDP3 - qpCremnPop3  #pC5 is the fifth paired county (Trumbull here)\n",
    "            qpC_PCP = get_PCP(qpCremnList3,tractCP, tractPop3)\n",
    "            pC5cT = getClosestTract(qpC_PCP,countyTractList[pC5],tractCP)  #force a northern Trumbull into Asht-Tru straddler\n",
    "            pC5Angles = [math.pi* (0.01+ (0.99-0.01)*float(n+0.5)/nTrumCuts) for n in range(nTrumCuts)]  #all cutting angles\n",
    "            for pC5angle in pC5Angles:\n",
    "                if angledPentaPoly(countyPCP[pC5],pC5angle,5.*countyGeom[pC5].area **0.5).disjoint(tractCP[pC5cT]):\n",
    "                    pC5angle += math.pi #oops, need complementary angle to capture\n",
    "                pC5list, pC5snapPop = cutVtdSnap(pC5tgtPop, tolr5, tractCP, countyTractList[pC5],neighborList,tractPop3,\n",
    "                                                pC5angle, countyPCP[pC5]) \n",
    "                pC5list3, pC5ML3, pC5snapPop3, splitMuni = cutMuniSnap(pC5tgtPop3, tolr5, muniTractList, tractCP, countyTractList[pC5],\n",
    "                                                                     muniNeighborList,tractPop3, muniPop, muniPCP, pC5angle, countyPCP[pC5])  \n",
    "                popGap = pC5tgtPop3 - pC5snapPop3\n",
    "                if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "                    if popGap < 0  :\n",
    "                        print(\"munisnap pop of\",pC5snapPop3,\"vs target\",pC5tgtPop3,\"in\",pC5,\"and\",qpCremnPop3,\"in cty\",qpC)\n",
    "                        raise Exception(\"error! pC5snapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "                    pC5list3B, pC5snapPop3B, nSplits = tryNoSplit(t, popGap, tolr5, aDP, muniPop, muniPCP, muniTractList,muniNeighborList,\n",
    "                                                                  pC5ML3,countyMuniList[pC5],tractCP, neighborList,tractPop3,0)        \n",
    "                    pC5list3, pC5snapPop3 = pC5list3 + pC5list3B, pC5snapPop3 + pC5snapPop3B\n",
    "                qpCpC5list, qpCpC5list3 = qpCremnList + pC5list, qpCremnList3 + pC5list3\n",
    "                qpCpC5Pop, qpCpC5Pop3   = qpCremnPop + pC5snapPop, qpCremnPop3 + pC5snapPop3\n",
    "                pC5onlyList, pC5onlyList3, pC5onlyPop, pC5onlyPop3 = list(), list(), 0., 0.\n",
    "                for v in countyTractList[pC5]:\n",
    "                    if v not in qpCpC5list:\n",
    "                        pC5onlyList.append(v)\n",
    "                        pC5onlyPop += tractPop3[v]\n",
    "                    if v not in qpCpC5list3:\n",
    "                        pC5onlyList3.append(v)\n",
    "                        pC5onlyPop3 += tractPop3[v]\n",
    "                isAllJiggy = True\n",
    "                for i, pair in enumerate([ [qpCpC5list, qpCpC5Pop], [qpCpC5list3, qpCpC5Pop3] ]):\n",
    "                    if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < toler):\n",
    "                        isAllJiggy = False\n",
    "                        #print(\"darn, we made a bad qpCpC5 district\",i,\"(0 for vtd snap, 1 for muni) with pop\",int(pair[1]))\n",
    "                        #for v in pair[0]:\n",
    "                        #    plotPoly(tractGeom[v])\n",
    "                        #plotPoly(countyGeom[spC])\n",
    "                        #plotPoly(countyGeom[tpC])\n",
    "                        #plt.show()\n",
    "                for i, pair in enumerate([ [pC5onlyList, pC5onlyPop], [pC5onlyList3, pC5onlyPop3] ]):\n",
    "                    if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < toler):\n",
    "                        isAllJiggy = False\n",
    "                        #print(\"darn, we made a bad pC5-only district\",i,\"(0 for vtd snap, 1 for muni) with pop\",int(pair[1]))\n",
    "                        #for v in pair[0]:\n",
    "                        #    plotPoly(tractGeom[v])\n",
    "                        #plotPoly(countyGeom[spC])\n",
    "                        #plotPoly(countyGeom[tpC])\n",
    "                        #plt.show()\n",
    "                if isAllJiggy:  #all four districts (StarkPortGea, Gea-Asht, Asht-Trum, Trumbull-only) are legit for vtd- and muni-snap\n",
    "                    nGood[jj] +=1\n",
    "                    #print(\"Hooray! Made a 3-district GeaAshTrum set from cut angles\",r3(57.296*qpCangle),r3(57.296*pC5angle))\n",
    "                    GeaAshTrumBLISTS.append( [tpCqpClist, qpCpC5list, pC5onlyList] )\n",
    "                    GeaAshTrumBLISTS3.append( [tpCqpClist3, qpCpC5list3, pC5onlyList3] )\n",
    "                    GeaAshTrumBTriggers.append(jj)\n",
    "                else:\n",
    "                    nFail[jj] +=1\n",
    "    print(\"all done making GeaAshTrum sets for StarkPortGea starter no\",jj,\". nGood, nFailed sets =\",nGood[jj],nFail[jj])\n",
    "    print(\"total should be\",nAshCuts,\" x \",nTrumCuts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "52a3a30b-27ad-4690-a486-59c66789c018",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the Gea-Asht-Trum 3B coupled 3-district lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the Gea-Asht-Trum 3B coupled 3-district lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "date = \"03Apr\"\n",
    "rLISTS = [GeaAshTrumBLISTS.copy(), GeaAshTrumBLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "pointerList = GeaAshTrumBTriggers.copy()\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    triPointer = list() \n",
    "    #dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    planNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            #dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            planNo.append(p)\n",
    "            triPointer.append(pointerList[p])    \n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"planNo\":planNo,\"triCounty pointer\":triPointer,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"GeaAshtTrum3B\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7bfb7e74-5751-456f-8ceb-579e06134e45",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I read in 16 , 16 vtd- , muni-snapped tract lists for Stark-Portage-Geauga triCounty\n"
     ]
    }
   ],
   "source": [
    "#4/2/23 - not sure why below is only delivering a small muni-snap pop for the NE district\n",
    "#cold restart -- read in the SPGtriLists\n",
    "vtdSnapDF = pd.read_csv(\"state_HD_output/OH02AprStarkPortGea_vtd.csv\")\n",
    "muniSnapDF = pd.read_csv(\"state_HD_output/OH02AprStarkPortGea_muni.csv\")\n",
    "SPG_TLS = vtdSnapDF['HDtractList']\n",
    "SPG_TLS3 = muniSnapDF['HDtractList']\n",
    "SPGtriLists =  [ast.literal_eval(SPG_TLS[i])  for i in range(len(SPG_TLS)) ]\n",
    "SPGtriLists3 = [ast.literal_eval(SPG_TLS3[i]) for i in range(len(SPG_TLS3)) ]\n",
    "print(\"I read in\",len(SPGtriLists),\",\",len(SPGtriLists3),\"vtd- , muni-snapped tract lists for Stark-Portage-Geauga triCounty\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "d9dc155d-77ce-4c1a-8d2b-2be8052d234e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now we create three whole Stark districts for schemeB.  Reapply schemeA's approach (NE cut, then bisect)\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 0\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 0\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 0\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 0\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 0\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 1\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 1\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 1\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 1\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 1\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 2\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 2\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 2\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 2\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 2\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 3\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 3\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 3\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 3\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 3\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 4\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 4\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 4\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 4\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 4\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 5\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 5\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 5\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 5\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 5\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 6\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 6\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 6\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 6\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 6\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 7\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 7\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 7\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 7\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 7\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 8\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 8\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 8\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 8\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 8\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 9\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 9\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 9\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 9\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 9\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 10\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 10\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 10\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 10\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 10\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 11\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 11\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 11\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 11\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 1794.0 True t,i,vtd-pop,contiguity = FAIL\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "7839 2 229885.0 True t,i,vtd-pop,contiguity = FAIL\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 11\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 12\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 12\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 12\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 12\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 12\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 13\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 13\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 13\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 13\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 13\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 14\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 14\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 14\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 14\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 14\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 15\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 15\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 15\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 15\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 15\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 16\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 16\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 16\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 16\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 16\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 17\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 17\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 17\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 17\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 17\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 18\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 18\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 18\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "7839 1 111667.0 True t,i,muni-pop,contiguity = FAIL\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 18\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 18\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are 348306.0 348306.0 116102 116102 2874\n",
      "now trying angle 0 of 5 angle deg = 90.0 for triArea district 19\n",
      "cutMuniSnap yielded a pop of 104451.0 vs target 116102.0\n",
      "now trying angle 1 of 5 angle deg = 103.5 for triArea district 19\n",
      "cutMuniSnap yielded a pop of 100633.0 vs target 116102.0\n",
      "now trying angle 2 of 5 angle deg = 117.0 for triArea district 19\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 3 of 5 angle deg = 130.5 for triArea district 19\n",
      "cutMuniSnap yielded a pop of 118475.0 vs target 116102.0\n",
      "now trying angle 4 of 5 angle deg = 144.0 for triArea district 19\n",
      "cutMuniSnap yielded a pop of 114914.0 vs target 116102.0\n",
      "all done attempting 3 Stark schemeB districts.  We had a total of 89 successes and 11 fails\n"
     ]
    }
   ],
   "source": [
    "print(\"now we create three whole Stark districts for schemeB.  Reapply schemeA's approach (NE cut, then bisect)\")\n",
    "print(\"try 5 angles for Stark whole #1, then take most legit compact of ten 2-district cuts for Stark 2+3\")\n",
    "finalStarkBLISTS, finalStarkBLISTS3, finalStarkBAngles = list(), list(),list()\n",
    "Stark3BdiNo = list()  #pointer back to the Stark triCounty partial that triggered this 3-district Stark draw\n",
    "minAngle, maxAngle, nAngles = 0.5 * math.pi, 0.8 * math.pi, 5\n",
    "j = 2\n",
    "c = 75  #Stark County\n",
    "nStarkFail, nStarkGood = 0,0\n",
    "for iii, L in enumerate(SPGtriLists):\n",
    "    remnList, remnList3, remnPop, remnPop3 = list(), list(), 0., 0.\n",
    "    bannedVtdList = list()\n",
    "    for v in countyTractList[c] :\n",
    "        if v not in L:\n",
    "            remnList.append(v)\n",
    "            remnPop += tractPop3[v]\n",
    "        if v not in SPGtriLists3[iii]:\n",
    "            remnList3.append(v)\n",
    "            remnPop3 += tractPop3[v]\n",
    "    CaDP =  remnPop  / int(countyPop[c]/aDP)  #shade targets and tolerances to maximize chance of all three good districts\n",
    "    CaDP3 = remnPop3 / int(countyPop[c]/aDP)\n",
    "    Ctoler, Ctoler3 = min(CaDP - 0.95*aDP, 1.05*aDP - CaDP) , min(CaDP3 - 0.95*aDP, 1.05*aDP - CaDP3)\n",
    "    print(\"our remnPop, remnPop3, CaDP, CaDP3, Ctoler3 are\",remnPop, remnPop3, int(CaDP), int(CaDP3), int(Ctoler3) )\n",
    "    for angleNo in range(nAngles):\n",
    "        angle = minAngle + (maxAngle - minAngle) * angleNo/ float(nAngles - 1)\n",
    "        print(\"now trying angle\",angleNo,\"of\",nAngles,\"angle deg =\",r3(angle*180./3.1416),\"for triArea district\",iii )\n",
    "        StarkLISTS, StarkPops   = [list(), list(), list()], [0., 0., 0.]\n",
    "        StarkLISTS3, StarkPops3 = [list(), list(), list()], [0., 0., 0.]\n",
    "        NEcorner = getClosestTract(Point(-81.08, 40.95), remnList, tractCP)  #ID a Stark NE corner tract to force it into Stark D1\n",
    "        if angledPentaPoly(countyPCP[c], angle, 5.*countyGeom[c].area**0.5 ).disjoint(tractCP[NEcorner]) :\n",
    "            angle += math.pi\n",
    "        StarkLISTS[0], StarkPops[0] = cutVtdSnap(CaDP, Ctoler, tractCP, remnList,neighborList,tractPop3, angle, countyPCP[c], NEcorner)\n",
    "        Stark0List3 = remnList3.copy()\n",
    "        forcedList3, forcedPop3 = list(), 0.  #this is a legacy from constrained optionA.  No forcing needed for schemeB\n",
    "        #for v in countyTractList[c]:\n",
    "        #    if tractMuniNo[v] in forcedMunis[0] and v in remnList3:  #we don't let the NE Stark district pick up the banned triArea remnant\n",
    "        #        Stark0List3.remove(v)\n",
    "        #        forcedList3.append(v)\n",
    "        #        forcedPop3 += tractPop[v]\n",
    "        aDP0 = CaDP3 - forcedPop3  #the target pop in the 0th in-Stark district, not including the forced pop from muni 318's remnant\n",
    "        list3, cML3, pop3, splitMuni = cutMuniSnap(aDP0, Ctoler3, muniTractList, tractCP, Stark0List3, muniNeighborList,tractPop3,\n",
    "                                                   muniPop, muniPCP, angle, countyPCP[c])\n",
    "        print(\"cutMuniSnap yielded a pop of\",pop3,\"vs target\",aDP0)\n",
    "        popGap = aDP0 - pop3\n",
    "        list3B, pop3B = list(), 0.\n",
    "        if (abs(popGap) > Ctoler3) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "            if popGap < 0  :\n",
    "                raise Exception(\"error! pop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            cMLremn = countyMuniList[c].copy()\n",
    "            #for m in forcedMunis[0]:\n",
    "            #    if m in countyMuniList[c]:\n",
    "            #        cMLremn.remove(m)  #banning the double-pickup of 318's partial\n",
    "            list3B, pop3B = getFillList3noSplit(NEcorner, popGap, Ctoler3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                cML3, cMLremn,tractCP, neighborList,tractPop3)\n",
    "            #not allowing a split because we are absorbing 318's partial\n",
    "            if pop3B == 0. :\n",
    "                print(\"uh oh, failed to muni-snap in county\",c,\"with angle\",angle,\"we are short by\",popGap)\n",
    "        \n",
    "        StarkLISTS3[0], StarkPops3[0] = forcedList3 + list3 + list3B, forcedPop3 + pop3 + pop3B  #this is the E / NE Stark whole district.\n",
    "        if not (isContiguous(StarkLISTS[0],neighborList) and isContiguous(StarkLISTS[0],neighborList)) :\n",
    "            print(r3(180./math.pi),\"deg cut for Stark0 created a discontiguous vtd- or muni NE Stark district.  Give up on this HD\")\n",
    "            nStarkFail +=1\n",
    "            continue          \n",
    "        #below - Now bisect remainder.  modified from Lorain\n",
    "        doubleList, doubleList3, doublePop, doublePop3 = list(), list(), 0., 0.\n",
    "        for v in remnList:\n",
    "            if v not in StarkLISTS[0]:\n",
    "                doubleList.append(v)\n",
    "                doublePop  += tractPop3[v]\n",
    "        muniPool3 = list()  #the list of munis in remaining 2-district set (we did not allow splitMuni in prev cut)\n",
    "        for v in remnList3:\n",
    "            if v not in StarkLISTS3[0] :\n",
    "                doubleList3.append(v)\n",
    "                doublePop3 += tractPop3[v]\n",
    "                if tractMuniNo[v] not in muniPool3:  #triDistrict + Stark district0 do not bequeath any split muni's to d1 or d2\n",
    "                    muniPool3.append(tractMuniNo[v])\n",
    "        if not (isContiguous(doubleList,neighborList) and isContiguous(doubleList3,neighborList)) :\n",
    "            print(r3(180./math.pi),\"deg cut for Stark0 left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\")\n",
    "            nStarkFail +=1\n",
    "            continue\n",
    "\n",
    "        #for i, HL in enumerate(halfList):  #revised Dayton code -- here, only one more cut to make, not 2 halves\n",
    "        HL = doubleList.copy()  #reusing Dayton nomenclature\n",
    "        off2Pop = abs(0.5*doublePop - aDP)\n",
    "        TOL = max( 0.02*aDP, 0.05*aDP - 0.5*off2Pop)  #tighten if 2-district off\n",
    "        qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "        minAngle2, maxAngle2 = 0., math.pi,   \n",
    "        qAngle0 = random.uniform(minAngle, maxAngle)  #any cut may work\n",
    "\n",
    "        bestDlists = [list(), list(), list(), list()]\n",
    "        nAngleTries,lowScore = 10, 999999.\n",
    "        qLoopNo = 0\n",
    "        foundGoodAngle = False\n",
    "        while qLoopNo < nAngleTries:  #try multiple angles, save the districts with lowest score if all districts legit\n",
    "            qLoopNo +=1\n",
    "            qAngle = qAngle0 + math.pi * float(qLoopNo)/nAngleTries\n",
    "            #print(\"trying angle\",qAngle*180./3.1416,\"for 1st cut angle\",angle*180./3.1416)\n",
    "            dblList, dblPop = [list(), list()], [0., 0.]\n",
    "            dblList[0], dblPop[0] = cutVtdSnap(0.5*doublePop, TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)\n",
    "            for tt in HL:\n",
    "                if tt not in dblList[0]:  #throw all remaining vtds into 2nd whole Lorain district\n",
    "                    dblList[1].append(tt)\n",
    "            dblPop[1] = doublePop - dblPop[0]\n",
    "\n",
    "            HL3 = doubleList3.copy()  #reusing Dayton code\n",
    "            dblList3, dblPop3 = [list(), list()], [0., 0.]\n",
    "            qCutPoint3 = get_PCP(HL3, tractCP, tractPop3)  #note: reusing same angle as vtd-snapped (horiz,diag or vert)\n",
    "            off2Pop3 = abs(0.5*doublePop3 - aDP)\n",
    "            TOL3 = max( 0.02*aDP, toler - 0.5*off2Pop3)  #tighten if 2-district off\n",
    "            dblList3[0], dML, dblPop3[0], splitMuni = cutMuniSnap(0.5*doublePop3, TOL3, muniTractList,tractCP, HL3,\n",
    "                                                                  muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "            popGap = 0.5*doublePop3 - dblPop3[0]\n",
    "            list3B, pop3B = list(),0.\n",
    "            if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "                hT = getFarthestTract(qCutPoint3, dblList3[0], tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "                list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,dML,\n",
    "                                                          muniPool3,tractCP, neighborList,tractPop3,0)\n",
    "            dblList3[0], dblPop3[0] = dblList3[0] + list3B, dblPop3[0] + pop3B\n",
    "            for tt in HL3:\n",
    "                if tt not in dblList3[0]:  #throw all remaining vtds into 3rd whole Lucas district\n",
    "                    dblList3[1].append(tt)\n",
    "            dblPop3[1] = doublePop3 - dblPop3[0]\n",
    "            isJiggy = True\n",
    "            score = 0.\n",
    "            currentLists,currentPops = [dblList[0], dblList[1], dblList3[0], dblList3[1]] ,[dblPop[0],dblPop[1], dblPop3[0], dblPop3[1] ]\n",
    "            for L in currentLists:\n",
    "                score += compactScore(L,tractCP, tractPop3)\n",
    "                if not isContiguous(L, neighborList):\n",
    "                    isJiggy = False\n",
    "            for p in currentPops :\n",
    "                if abs(p - aDP) > toler:\n",
    "                    isJiggy = False\n",
    "            if isJiggy and score < lowScore:\n",
    "                foundGoodAngle = True\n",
    "                lowScore = score\n",
    "                bestDlists = currentLists.copy()\n",
    "                bestDpops =  currentPops.copy()\n",
    "                bestAngle = qAngle\n",
    "\n",
    "        if foundGoodAngle:  #overwrite.  If we didn't overwrite, we'll catch the fails in the nGood block\n",
    "            dblList[0], dblList[1], dblPop[0], dblPop[1] = bestDlists[0], bestDlists[1], bestDpops[0], bestDpops[1]\n",
    "            dblList3[0], dblList3[1], dblPop3[0], dblPop3[1] = bestDlists[2], bestDlists[3], bestDpops[2], bestDpops[3]\n",
    "        for i, L in enumerate(dblList3):\n",
    "            StarkLISTS[i+1] = dblList[i]\n",
    "            StarkLISTS3[i+1] = dblList3[i]\n",
    "            StarkPops[i+1] = dblPop[i]\n",
    "            StarkPops3[i+1] = dblPop3[i]\n",
    "        nGood, nGood3 = 0,0\n",
    "        for i, L in enumerate(StarkLISTS):\n",
    "            if abs(StarkPops[i] - aDP) <= 0.05 * aDP and isContiguous(StarkLISTS[i],neighborList):\n",
    "                nGood +=1\n",
    "            else:\n",
    "                print(t,i,StarkPops[i],isContiguous(StarkLISTS[i],neighborList),\"t,i,vtd-pop,contiguity = FAIL\")\n",
    "            if abs(StarkPops3[i] - aDP) <= 0.05 * aDP and isContiguous(StarkLISTS3[i],neighborList):\n",
    "                nGood3 +=1\n",
    "            else:\n",
    "                print(t,i,StarkPops3[i],isContiguous(StarkLISTS3[i],neighborList),\"t,i,muni-pop,contiguity = FAIL\")\n",
    "\n",
    "        if nGood == 3 and nGood3 == 3:\n",
    "            nStarkGood +=1\n",
    "            finalStarkBLISTS.append( [StarkLISTS[0],  StarkLISTS[1],   StarkLISTS[2] ] )\n",
    "            finalStarkBLISTS3.append([StarkLISTS3[0], StarkLISTS3[1],  StarkLISTS3[2] ] )\n",
    "            finalStarkBAngles.append( [angle,bestAngle] )\n",
    "            Stark3BdiNo.append(iii)\n",
    "        else:\n",
    "            nStarkFail +=1\n",
    "print(\"all done attempting 3 Stark schemeB districts.  We had a total of\",nStarkGood,\"successes and\",nStarkFail,\"fails\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "d02b6627-c126-41ab-8ef3-d3eaa3f7ccf6",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "let us write the whole Stark 3B coupled 3-district lists to a file.  We will vote later\n"
     ]
    }
   ],
   "source": [
    "print(\"let us write the whole Stark 3B coupled 3-district lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "\n",
    "date = \"03Apr\"\n",
    "rLISTS = [finalStarkBLISTS.copy(), finalStarkBLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "pointerList = Stark3BdiNo.copy()\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    triPointer = list() \n",
    "    #dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    planNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            #dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            planNo.append(p)\n",
    "            triPointer.append(pointerList[p])    \n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"planNo\":planNo,\"triCounty pointer\":triPointer,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"Stark3B\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "ccdda3f9-07fa-4ef3-9094-6308033ef06d",
   "metadata": {},
   "source": [
    "#Lake code for full 2-district Lake\n",
    "#3/13/23 COMPLETE - LAKE COUNTY: apply Dayton list-storing approach to Lake with simple 2-district cut\n",
    "mC, mCTL = 42, countyTractList[mC]  #Lake\n",
    "aDP = avgDistrictPop\n",
    "targPop = countyPop[mC] / round(countyPop[mC]/avgDistrictPop,0)\n",
    "tolrPop = min(1.05 * avgDistrictPop - targPop, targPop - 0.95*avgDistrictPop)\n",
    "print(targPop, tolrPop,\"are target and tolrPop based on countyPop/aDP =\",r3(countyPop[mC]/avgDistrictPop) )\n",
    "\n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "LISTS, LISTS3 = list(), list()\n",
    "nCuts = 15\n",
    "minAngle,maxAngle = 0.4*math.pi, 0.95 * math.pi\n",
    "for s in range(nCuts):\n",
    "    sliceAngle = minAngle + (maxAngle-minAngle) * float(s)/nCuts\n",
    "    cutPoint = countyPCP[mC]\n",
    "    halfList, halfPop = cutVtdSnap(targPop, tolrPop, tractCP, mCTL, neighborList, tractPop, sliceAngle, cutPoint)\n",
    "    #print(\"our final halfPop vs aDP is\",halfPop, targPop,'for angle (deg) =',r3(sliceAngle*180./math.pi))\n",
    "    #for v in halfList:\n",
    "    #    plotPoly(tractGeom[v])\n",
    "    #plotPoly(countyGeom[mC])\n",
    "    #DR = 0.2\n",
    "    #plt.plot([cutPoint.x - DR * math.cos(sliceAngle), cutPoint.x + DR * math.cos(sliceAngle)],\n",
    "    #         [cutPoint.y - DR * math.sin(sliceAngle), cutPoint.y + DR * math.sin(sliceAngle)] )  #plotting the original pizza cut\n",
    "    #plt.show()\n",
    " \n",
    "    halfList3, hML, halfPop3, splitMuni = cutMuniSnap(targPop, tolrPop, muniTractList,tractCP, mCTL,\n",
    "                                                  muniNeighborList,tractPop3, muniPop, muniPCP, sliceAngle, cutPoint)\n",
    "    popGap = targPop - halfPop3\n",
    "    list3B, pop3B = list(),0.\n",
    "    if abs(popGap) > tolrPop :  #SPECIAL for Lake, we allow a single split; Mentor is problematic to skirt.\n",
    "        splitCutPt = muniPCP[splitMuni]\n",
    "        list3B, pop3B = cutVtdSnap(popGap, tolrPop,tractCP, muniTractList[splitMuni], neighborList, tractPop, sliceAngle, splitCutPt)\n",
    "        halfList3, halfPop3 = halfList3 + list3B, halfPop3 + pop3B\n",
    "    #print(\"our final halfPop3 vs aDP is\",halfPop3, targPop,'for angle (deg) =',r3(sliceAngle*180./math.pi))\n",
    "    #for v in halfList3:\n",
    "    #    plotPoly(tractGeom[v])\n",
    "    #plotPoly(countyGeom[mC])\n",
    "    #DR = 0.2\n",
    "    #plt.plot([cutPoint.x - DR * math.cos(sliceAngle), cutPoint.x + DR * math.cos(sliceAngle)],\n",
    "    #        [cutPoint.y - DR * math.sin(sliceAngle), cutPoint.y + DR * math.sin(sliceAngle)] )  #plotting the original pizza cut\n",
    "    #plt.show()\n",
    "    halfListB, halfList3B, halfPopB, halfPop3B = list(), list(), 0., 0.\n",
    "    for v in mCTL:\n",
    "        if v not in halfList:\n",
    "            halfListB.append(v)\n",
    "            halfPopB += tractPop3[v]\n",
    "        if v not in halfList3:\n",
    "            halfList3B.append(v)\n",
    "            halfPop3B += tractPop3[v]\n",
    "    if np.min([halfPop, halfPopB, halfPop3, halfPop3B]) > 0.95*aDP and np.max([halfPop, halfPopB, halfPop3, halfPop3B]) < 1.05*aDP:\n",
    "        if isContiguous(halfList,neighborList) and isContiguous(halfListB,neighborList):\n",
    "            if isContiguous(halfList3,neighborList) and isContiguous(halfList3B,neighborList):\n",
    "                print(r3(180.*sliceAngle/math.pi),\"is a good deg Angle for creating 2 districts in county\",mC)\n",
    "                LISTS.append( [halfList,  halfListB])\n",
    "                LISTS3.append([halfList3, halfList3B])\n",
    "        else:\n",
    "            print(r3(180.*sliceAngle/math.pi),\"FAILED TO create 2 districts in county\",mC)\n",
    "print(\"we created\",len(LISTS),\"good 2-district plans out of\",nCuts,\"tries in county\",mC)    \n",
    "LakeLISTS, LakeLISTS3 = LISTS.copy(), LISTS3.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2cbba913-57c8-43c4-873c-0ff384d4da6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"4/3/23: Yayyyyy! Scheme B now fully specified except Cuyahoga 10-district options\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e9d6b4d-365b-4202-873c-b57bc10b271a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "raw",
   "id": "7f6e1955-c6cf-40e8-9610-e36a3930ef3d",
   "metadata": {},
   "source": [
    "#BELOW - COLLATED 'OPTION A' CODE BLOCKS (stored 4/9/23 - no longer using schemeA)\n",
    "\n",
    "coupledList = [mC, ppC, spC, tpC]  #list of coupled counties for optionA\n",
    "regional_aDP = 0.\n",
    "regionalPop = 0.\n",
    "mC = coupledList[0]\n",
    "for c in coupledList:\n",
    "    regionalPop += countyPop[c]\n",
    "regionalDistricts = round(regionalPop/aDP,0)\n",
    "regional_aDP_ratio = regionalPop / regionalDistricts / aDP\n",
    "print(\"county group\",coupledList,\"forms\",regionalDistricts,\"districts with avgDistrictPop of\",r3(regional_aDP_ratio),\"vs normal avg\")\n",
    "nonBigPop = regionalPop - countyPop[mC]\n",
    "naiveNonBigWholePop = nonBigPop + countyPop[mC]%aDP  #if we didn't shade the straddler in-county pop to ease the constraint\n",
    "nonBigDistricts = round(naiveNonBigWholePop / aDP, 0)\n",
    "targetICP = aDP - nonBigPop%aDP   #now, shading the in-county straddler pop; easy to recover in many big whole districts\n",
    "naive_RaDP = naiveNonBigWholePop / nonBigDistricts  #regional avgDistrictPop in the coupled region (excluding whole-in-big-county districts)\n",
    "naiveRatio = naive_RaDP / aDP\n",
    "adjBigCountyADP = round((countyPop[mC] - targetICP)/int(countyPop[mC]/aDP), 0)\n",
    "RaDP = (nonBigPop + targetICP) / nonBigDistricts  #regional avgDistrictPop (may be shaded)\n",
    "print(\"if we naively drew all districts in this group to same target avgDistrictPop of\",int(aDP),\"...\")\n",
    "print(\"...we need a target aDP of \",naive_RaDP,\"=\",r3(naiveRatio),\"of normal in the districts not wholly in big county\",mC)\n",
    "print(\"but the minimum districtPop is\",int(0.95*aDP),\"so we are constrained\")\n",
    "print(\"Instead, we will shade the in-county partial to\",r3(targetICP),\"vs naive\",r3(countyPop[mC]%aDP),\"to ease small-county constraints.\")\n",
    "print(\"The new target aDP in big-county whole districts is now\",r3(adjBigCountyADP),\", but\",r3(RaDP),\"outside bigCounty = normal\",int(aDP))\n",
    "\n",
    "\n",
    "countyStraddlerList[mC] = [3953, 5036]\n",
    "cutAngle = [1.6*math.pi, 1.2*math.pi]\n",
    "toler = 0.05*aDP\n",
    "CuyaGeaLISTS, CuyaGeaLISTS3 = list(), list()  #the vtd- and muni-snapped tract list for each Cuyahoga-Geauga straddler district\n",
    "for i, t in enumerate(countyStraddlerList[mC]):\n",
    "    homeM = tractMuniNo[t]\n",
    "    nSplits = 0  #tracking total splits in all counties composing this straddler district.  Force to 0-1\n",
    "    #targetICP = countyPop[mC]%aDP  #target in-county Pop.  see above block for special target for this region\n",
    "\n",
    "    #spC = spcGroups[pairedCountyList.index(ppC)]  #for Delaware, no spC needed   \n",
    "    mCangle = cutAngle[i] #getSliceAngle(tractCP[t],stdCutPoint,tractCP[t]) + random.uniform(-1.*angleNoise,angleNoise)\n",
    "    #print(r3(180./3.1416*mCangle), \"is our computed slice angle in deg for tract\",t)\n",
    "    stdCutPoint = Point(0.3*countyPCP[mC].x+0.7*tractCP[t].x, 0.3*countyPCP[mC].y+0.7*tractCP[t].y)\n",
    "    #plotPoly(angledPentaPoly(stdCutPoint,mCangle,stdCutPoint.distance(tractCP[t]) ) )\n",
    "    #plotCenter(\"sCP\",stdCutPoint)\n",
    "    #plotPoly(tractCP[t].buffer(0.01))\n",
    "    #plotPoly(countyGeom[mC])\n",
    "    #plt.show()\n",
    "    mClist, mCsnapPop = cutVtdSnap(targetICP, toler, tractCP, countyTractList[mC],neighborList,tractPop3, mCangle, countyPCP[mC],t)\n",
    "    #tried cut HDvtdSnap and HDmuniSnap but they often left discontiguous 2-district remnants\n",
    "    mClist3, mcML3, mCsnapPop3, splitMuni = cutMuniSnap(targetICP, toler, muniTractList, tractCP, countyTractList[mC],\n",
    "                                                        muniNeighborList,tractPop3, muniPop, muniPCP, mCangle, countyPCP[mC])\n",
    "    popGap = targetICP - mCsnapPop3\n",
    "    mClist3B, mCsnapPop3B = list(),0.\n",
    "    if (abs(popGap) > toler) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "        if popGap < 0  :\n",
    "            raise Exception(\"error! mCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "        mClist3B, mCsnapPop3B = getFillList3noSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                    mcML3, countyMuniList[mC],tractCP, neighborList,tractPop3)\n",
    "        if mCsnapPop3B == 0. :\n",
    "            print(\"uh oh, failed to create whole-muni in inCounty part of straddler district for tract\",t,\"we are short by\",popGap)\n",
    "        \n",
    "        mClist3, mCsnapPop3 = mClist3 + mClist3B, mCsnapPop3 + mCsnapPop3B  #completing in the inCounty partial despite splitM flag\n",
    "    \n",
    "    snappedHDpop[t] = mCsnapPop\n",
    "    snappedHD3pop[t] = mCsnapPop3\n",
    "    HDtractList[t]  = mClist.copy()\n",
    "    HDtractList3[t] = mClist3.copy()\n",
    "    print(\"t,mCsnapPop, mCsnapPop3 are\",t,mCsnapPop, mCsnapPop3,\"vs tgt ICP\",targetICP)\n",
    "    if countyPop[ppC] > 1.05* aDP - mCsnapPop:\n",
    "        isFullppC, isFullppC3 = False, False  #always true for NE.  See Dayton for adding full ppC + part spC\n",
    "    ppCcT = getClosestTract(tractCP[t],countyTractList[ppC],tractCP)\n",
    "    ppCangle = getSliceAngle(tractCP[ppCcT],countyPCP[ppC],tractCP[ppCcT])\n",
    "    if not isFullppC:\n",
    "        ppCtgtPop = RaDP - mCsnapPop\n",
    "        ppClist, ppCsnapPop = cutVtdSnap(ppCtgtPop, toler, tractCP, countyTractList[ppC],neighborList,tractPop3,\n",
    "                                        ppCangle, countyPCP[ppC]) \n",
    "    if not isFullppC3:\n",
    "        ppCtgtPop3 = RaDP - mCsnapPop3\n",
    "        ppClist3, ppcML3, ppCsnapPop3, splitMuni = cutMuniSnap(ppCtgtPop3, toler, muniTractList, tractCP, countyTractList[ppC],\n",
    "                                                             muniNeighborList,tractPop3, muniPop, muniPCP, ppCangle, countyPCP[ppC])  \n",
    "        popGap = ppCtgtPop3 - ppCsnapPop3\n",
    "        #print(t,int(ppCtgtPop3), ppCsnapPop3, \"t,tgt ppC3, actual\",ppcML3,\"=ppcML3\")\n",
    "        if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "            if popGap < 0  :\n",
    "                print(\"munisnap pop of\",mCsnapPop3,\"in\",mC,\"and\",ppCsnapPop3,\"in cty\",ppC,\"vs tgt\",ppCtgtPop3)\n",
    "                raise Exception(\"error! ppCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            # 3/4/23 NOTE:  WE COULD TRY A WORKAROUND IF ppcML3 is blank.  Requires us to ID all ppC muni's contiguous to mC HDlist\n",
    "            ppClist3B, ppCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,ppcML3,\n",
    "                                                        countyMuniList[ppC],tractCP, neighborList,tractPop3,0)        \n",
    "            ppClist3, ppCsnapPop3 = ppClist3 + ppClist3B, ppCsnapPop3 + ppCsnapPop3B\n",
    "        #print(t,ppCsnapPop, ppCsnapPop3,\"ppC pops vs tgts\",int(ppCtgtPop), int(ppCtgtPop3))\n",
    "    \n",
    "    HDtractList[t]  += ppClist\n",
    "    HDtractList3[t] += ppClist3\n",
    "    snappedHDpop[t]  += ppCsnapPop\n",
    "    snappedHD3pop[t] += ppCsnapPop3\n",
    "    stradList, stradList3, stradPop, stradPop3 = HDtractList[t], HDtractList3[t], snappedHDpop[t], snappedHD3pop[t]\n",
    "\n",
    "    print(\"after adding ppC, t, HDpop and pop3 are\",t, snappedHDpop[t], snappedHD3pop[t])\n",
    "    if isContiguous(stradList,neighborList) and isContiguous(stradList3,neighborList) and (\n",
    "        stradPop>0.95*aDP and stradPop<1.05*aDP and stradPop3 >0.95*aDP and stradPop3<1.05*aDP ):\n",
    "        print(\"vtd\",t,\"has created a legit straddler district; add to our keeper straddler list for\",mC,\"-\",ppC,\"combo\")\n",
    "        CuyaGeaLISTS.append(stradList)\n",
    "        CuyaGeaLISTS3.append(stradList3)\n",
    "    else:\n",
    "        print(\"vtd\",t,\"created an illegitimate vtd- or muni-snapped district.  Do NOT add to keeper list\")\n",
    "    for v in HDtractList3[t]:\n",
    "        plotPoly(tractGeom[v])\n",
    "    plotPoly(countyGeom[mC])\n",
    "    plotPoly(countyGeom[ppC])\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "print(\"let us write the CuyaGea lists to a file.  We will vote later\" )    \n",
    "for rNo in range(nRegions):  #find which region has this county in it\n",
    "    if mC in regionCountyList[rNo]:\n",
    "        currentRegionNo = rNo\n",
    "date = \"01Apr\"\n",
    "rLISTS = [CuyaGeaLISTS.copy(), CuyaGeaLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo = list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "\n",
    "\n",
    "    #unindent and use for multidistrict plans\n",
    "    #dTL = list()  #tractlist for each district\n",
    "    #dWeight = list()\n",
    "    #dRegionNo = list()\n",
    "    \n",
    "    #for p,plan in enumerate(PLANS):  Use this block for multi-district plans\n",
    "    #    for d in plan:\n",
    "    #        dTL.append(d)\n",
    "    #        #dWeight.append(rWeights[i][p])\n",
    "    #        dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"CuyaGea\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)\n",
    "\n",
    "\n",
    "print(\"A: now we create options for the\",nonBigDistricts-1,\"remaining districts in the region for each of the above starters\")\n",
    "GeaAshTrumLISTS, GeaAshTrumLISTS3 = list(), list()\n",
    "nAshCuts, nTrumCuts = 6,10\n",
    "toler=0.05*aDP\n",
    "nFail = [0,0]\n",
    "nGood = [0,0]\n",
    "for j, L in enumerate(CuyaGeaLISTS):\n",
    "    ppCremnList, ppCremnList3 = list(), list()  #Geauga vtd's not drawn into the straddler\n",
    "    ppCremnPop, ppCremnPop3 = 0., 0.\n",
    "    for v in countyTractList[ppC]:  #assign these remaining Geauga vtd's to the next district\n",
    "        if v not in L:\n",
    "            ppCremnList.append(v)\n",
    "            ppCremnPop += tractPop3[v]\n",
    "        if v not in CuyaGeaLISTS3[j]:\n",
    "            ppCremnList3.append(v)\n",
    "            ppCremnPop3 += tractPop3[v]\n",
    "    spCtgtPop, spCtgtPop3 = RaDP - ppCremnPop, RaDP - ppCremnPop3\n",
    "    spCcT = getClosestTract(Point(-81., 41.8),countyTractList[spC],tractCP)  #force NW Ashtabula tract into Gea-Asht\n",
    "    #plotPoly(tractGeom[spCcT])\n",
    "    #plotPoly(countyGeom[spC])\n",
    "    #plt.show()\n",
    "    #pause = input(\"showing the forced spC tract\")\n",
    "    sAngles = [math.pi* (0.25+ (0.65-0.25)*float(n+0.5)/nAshCuts) for n in range(nAshCuts)]  #NE - N - NW cutting angles for Asht\n",
    "    for spCangle in sAngles:\n",
    "        if angledPentaPoly(countyPCP[spC],spCangle,5.*countyGeom[spC].area).disjoint(tractCP[spCcT]):\n",
    "            spCangle += math.pi #oops, need complementary angle to capture\n",
    "        #plotPoly(angledPentaPoly(countyPCP[spC],spCangle, 0.4*countyGeom[spC].area))\n",
    "        #plotPoly(tractGeom[spCcT])\n",
    "        #plt.show()\n",
    "        #pause = input(\"showing the spC penta w forced tract\")\n",
    "        spClist, spCsnapPop = cutVtdSnap(spCtgtPop, toler, tractCP, countyTractList[spC],neighborList,tractPop3,\n",
    "                                        spCangle, countyPCP[spC],spCcT) \n",
    "        spClist3, spcML3, spCsnapPop3, splitMuni = cutMuniSnap(spCtgtPop3, toler, muniTractList, tractCP, countyTractList[spC],\n",
    "                                                             muniNeighborList,tractPop3, muniPop, muniPCP, spCangle, countyPCP[spC])  \n",
    "        popGap = spCtgtPop3 - spCsnapPop3\n",
    "        if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "            if popGap < 0  :\n",
    "                print(\"munisnap pop of\",spCsnapPop3,\"vs tgt\",spCtgtPop3,\"and\",ppCsnapPop3,\"in cty\",ppC)\n",
    "                raise Exception(\"error! spCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            spClist3B, spCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,spcML3,\n",
    "                                                        countyMuniList[spC],tractCP, neighborList,tractPop3,0)        \n",
    "            spClist3, spCsnapPop3 = spClist3 + spClist3B, spCsnapPop3 + spCsnapPop3B\n",
    "        ppCspClist, ppCspClist3 = ppCremnList + spClist, ppCremnList3 + spClist3\n",
    "        ppCspCPop, ppCspCPop3   = ppCremnPop + spCsnapPop, ppCremnPop3 + spCsnapPop3\n",
    "        isJiggy = True\n",
    "        for i, pair in enumerate([ [ppCspClist, ppCspCPop], [ppCspClist3, ppCspCPop3] ]):\n",
    "            if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < toler):\n",
    "                isJiggy = False\n",
    "                print(\"darn, we made a bad ppC-spC district\",i,\"(0 for vtd snap, 1 for muni) with pop\",pair[1])\n",
    "                for v in pair[0]:\n",
    "                    plotPoly(tractGeom[v])\n",
    "                plotPoly(countyGeom[ppC])\n",
    "                plotPoly(countyGeom[spC])\n",
    "                plt.show()\n",
    "        if not isJiggy:\n",
    "            nFail[j] += nTrumCuts #we didn't get far enough to slice Trumbull, so all would have failed\n",
    "        if isJiggy:  #proceed to variations on an Asht-Trum and full Trumbull district to close out the set\n",
    "            #print(\"here was your ppC-spC district for muni-snapped\")\n",
    "            #for v in ppCspClist3:\n",
    "            #    plotPoly(tractGeom[v])\n",
    "            #plotPoly(countyGeom[spC])\n",
    "            #plt.show()\n",
    "            #pause = input(\"OK?\")\n",
    "            spCremnList, spCremnList3 = list(), list()  #Geauga vtd's not drawn into the straddler\n",
    "            spCremnPop, spCremnPop3 = 0., 0.\n",
    "            for v in countyTractList[spC]:  #assign these remaining Ashtabula vtd's to the next district\n",
    "                if v not in ppCspClist:\n",
    "                    spCremnList.append(v)\n",
    "                    spCremnPop += tractPop3[v]\n",
    "                if v not in ppCspClist3:\n",
    "                    spCremnList3.append(v)\n",
    "                    spCremnPop3 += tractPop3[v]\n",
    "            max_tpCtgtPop = countyPop[tpC]%aDP + 0.05*aDP\n",
    "            min_tpCtgtPop = countyPop[tpC]%aDP - 0.05*aDP\n",
    "            tpCtgtPop, tpCtgtPop3 = RaDP - spCremnPop, RaDP - spCremnPop3  #tpC is the tertiary pairing county (Trumbull here)\n",
    "            spC_PCP = get_PCP(spCremnList3,tractCP, tractPop3)\n",
    "            tpCcT = getClosestTract(spC_PCP,countyTractList[tpC],tractCP)  #force a northern Trumbull into Asht-Tru straddler\n",
    "            tAngles = [math.pi* (0.01+ (0.99-0.01)*float(n+0.5)/nTrumCuts) for n in range(nTrumCuts)]  #all cutting angles\n",
    "            for tpCangle in tAngles:\n",
    "                if angledPentaPoly(countyPCP[tpC],tpCangle,5.*countyGeom[tpC].area **0.5).disjoint(tractCP[tpCcT]):\n",
    "                    tpCangle += math.pi #oops, need complementary angle to capture\n",
    "                tpClist, tpCsnapPop = cutVtdSnap(tpCtgtPop, toler, tractCP, countyTractList[tpC],neighborList,tractPop3,\n",
    "                                                tpCangle, countyPCP[tpC]) \n",
    "                tpClist3, tpcML3, tpCsnapPop3, splitMuni = cutMuniSnap(tpCtgtPop3, toler, muniTractList, tractCP, countyTractList[tpC],\n",
    "                                                                     muniNeighborList,tractPop3, muniPop, muniPCP, tpCangle, countyPCP[tpC])  \n",
    "                popGap = tpCtgtPop3 - tpCsnapPop3\n",
    "                if (abs(popGap) > toler):  #primary pairing county missed pop target\n",
    "                    if popGap < 0  :\n",
    "                        print(\"munisnap pop of\",tpCsnapPop3,\"vs target\",tpCtgtPop3,\"in\",tpC,\"and\",spCremnPop3,\"in cty\",spC)\n",
    "                        raise Exception(\"error! spCsnapPop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "                    tpClist3B, tpCsnapPop3B, nSplits = tryNoSplit(t, popGap, toler, aDP, muniPop, muniPCP, muniTractList,muniNeighborList,\n",
    "                                                                  tpcML3,countyMuniList[tpC],tractCP, neighborList,tractPop3,0)        \n",
    "                    tpClist3, tpCsnapPop3 = tpClist3 + tpClist3B, tpCsnapPop3 + tpCsnapPop3B\n",
    "                spCtpClist, spCtpClist3 = spCremnList + tpClist, spCremnList3 + tpClist3\n",
    "                spCtpCPop, spCtpCPop3   = spCremnPop + tpCsnapPop, spCremnPop3 + tpCsnapPop3\n",
    "                tpConlyList, tpConlyList3, tpConlyPop, tpConlyPop3 = list(), list(), 0., 0.\n",
    "                for v in countyTractList[tpC]:\n",
    "                    if v not in spCtpClist:\n",
    "                        tpConlyList.append(v)\n",
    "                        tpConlyPop += tractPop3[v]\n",
    "                    if v not in spCtpClist3:\n",
    "                        tpConlyList3.append(v)\n",
    "                        tpConlyPop3 += tractPop3[v]\n",
    "                isAllJiggy = True\n",
    "                for i, pair in enumerate([ [spCtpClist, spCtpCPop], [spCtpClist3, spCtpCPop3] ]):\n",
    "                    if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < toler):\n",
    "                        isAllJiggy = False\n",
    "                        print(\"darn, we made a bad spC-tpC district\",i,\"(0 for vtd snap, 1 for muni) with pop\",int(pair[1]))\n",
    "                        #for v in pair[0]:\n",
    "                        #    plotPoly(tractGeom[v])\n",
    "                        #plotPoly(countyGeom[spC])\n",
    "                        #plotPoly(countyGeom[tpC])\n",
    "                        #plt.show()\n",
    "                for i, pair in enumerate([ [tpConlyList, tpConlyPop], [tpConlyList3, tpConlyPop3] ]):\n",
    "                    if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < toler):\n",
    "                        isAllJiggy = False\n",
    "                        print(\"darn, we made a bad tpC-only district\",i,\"(0 for vtd snap, 1 for muni) with pop\",int(pair[1]))\n",
    "                        #for v in pair[0]:\n",
    "                        #    plotPoly(tractGeom[v])\n",
    "                        #plotPoly(countyGeom[spC])\n",
    "                        #plotPoly(countyGeom[tpC])\n",
    "                        #plt.show()\n",
    "                if isAllJiggy:  #all four districts (CuyGea, Gea-Asht, Asht-Trum, Trumbull-only) are legit for vtd- and muni-snap\n",
    "                    nGood[j] +=1\n",
    "                    print(\"Hooray! Made a 3-district GeaAshTrum set w angles\",r3(57.296*spCangle),r3(57.296*tpCangle))\n",
    "                    GeaAshTrumLISTS.append( [ppCspClist, spCtpClist, tpConlyList] )\n",
    "                    GeaAshTrumLISTS3.append( [ppCspClist3, spCtpClist3, tpConlyList3] )\n",
    "                else:\n",
    "                    nFail[j] +=1\n",
    "    print(\"all done making GeaAshTrum sets for straddler-starter\",countyStraddlerList[mC][j],\". nGood, nFailed sets =\",nGood[j],nFail[j])\n",
    "    print(\"total should be\",nAshCuts,\" x \",nTrumCuts)\n",
    "\n",
    "\n",
    "print(\"let us write the GeaAshTrum optionA lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "date = \"01Apr\"\n",
    "rLISTS = [GeaAshTrumLISTS.copy(), GeaAshTrumLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    #dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    planNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            #dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            planNo.append(p)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"planNo\":planNo,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"GeaAshTrum\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)\n",
    "\n",
    "print(\"A: for the SummPortStark district, swell-grow each partial, forcing centerpoint inclusion\")\n",
    "print(\"allow multiple x-y skews for each partial, any combo of 3 partials that works will be stored as a triDistrict candidate\")\n",
    "print(\"candidates must be within districtPop toler, be contiguous, and have at most one total split in the three partials\")\n",
    "print(\"first, find the intersxn of the three counties (the triArea)\")\n",
    "triArea = countyGeom[SummPortStark[0]].buffer(0.02).intersection(countyGeom[SummPortStark[1]].buffer(0.02))\n",
    "triArea = triArea.intersection(countyGeom[SummPortStark[2]].buffer(0.02))\n",
    "triCP = triArea.centroid\n",
    "toler = 0.05*aDP\n",
    "#plotPoly(triArea)\n",
    "#for c in SummPortStark:\n",
    "#    plotPoly(countyGeom[c])\n",
    "#plt.show()\n",
    "bannedList = [1335] #, 1298 #exclude township that surrounds Kent from triDistrict, so Kent is not isolated in Portage-only\n",
    "cTlist = [countyTractList[SummPortStark[j]].copy() for j,k in enumerate(SummPortStark)]\n",
    "cMlist = [countyMuniList[SummPortStark[j]].copy()  for j,k in enumerate(SummPortStark)]\n",
    "for j,cML in enumerate(cMlist):\n",
    "    for m in bannedList:\n",
    "        if m in cML.copy():\n",
    "            cMlist[j].remove(m)  #don't let the triDistrict pick up this troublesome muni\n",
    "            for v in muniTractList[m]:\n",
    "                cTlist[j].remove(v)\n",
    "\n",
    "totAreaPop = 0.\n",
    "for c in SummPortStark:\n",
    "    totAreaPop += countyPop[c]\n",
    "RaDP = totAreaPop / round(totAreaPop/aDP,0)\n",
    "print(\"Now make partials.  In this region, we will aim for\",int(RaDP),\", not\",r3(aDP),\"district pops\")\n",
    "skewRatio = [ [0.4, 1.0, 2.5] for c in SummPortStark]  #the skew in the x vs. y length of each swollen partial district\n",
    "\n",
    "SummTriLISTS,  SummTriLISTS3,  SummTriPops,  SummTriPops3, SummTriSplits = list(), list(), list(), list(), list()\n",
    "PortTriLISTS,  PortTriLISTS3,  PortTriPops,  PortTriPops3, PortTriSplits = list(), list(), list(), list(), list()\n",
    "StarkTriLISTS, StarkTriLISTS3, StarkTriPops, StarkTriPops3, StarkTriSplits = list(), list(), list(), list(), list()\n",
    "SPS_LISTS, SPS_LISTS3 = [SummTriLISTS, PortTriLISTS, StarkTriLISTS],[SummTriLISTS3, PortTriLISTS3, StarkTriLISTS3]\n",
    "SPS_POPS, SPS_POPS3 = [SummTriPops, PortTriPops, StarkTriPops], [SummTriPops3, PortTriPops3, StarkTriPops3]\n",
    "SPS_SPLITS =          [SummTriSplits, PortTriSplits, StarkTriSplits]\n",
    "triLists, triLists3, splitLists3 = list(), list(), list()\n",
    "triToler = 0.6*0.05*aDP #a bit tighter since we need to add 3 partials\n",
    "fudgeFactor = 0.999    #it appears we are biasing low on our partial pops\n",
    "for i,c in enumerate(SummPortStark):\n",
    "    myLists, myPops, myLists3, myPops3 = SPS_LISTS[i], SPS_POPS[i],SPS_LISTS3[i], SPS_POPS3[i]\n",
    "    targetICP = fudgeFactor * RaDP/aDP * countyPop[c]%aDP\n",
    "    cTcP = tractCP[ getClosestTract(triCP, countyTractList[c],tractCP) ]\n",
    "    for xyR in skewRatio[i]:\n",
    "        Tlist, Tpop = vtdSwell(cTcP, targetICP, triToler, tractCP, cTlist[i],neighborList,tractPop3,xyR) \n",
    "        Tlist3, Tpop3, split3 = muniSwell(cTcP, targetICP, triToler, aDP, muniPop, muniPCP, muniTractList, \n",
    "                                          muniNeighborList,cMlist[i], tractCP, neighborList, tractPop3, 0, xyR)\n",
    "        #for v in Tlist3:\n",
    "        #    plotPoly(tractGeom[v])\n",
    "        #    plotPoly(tractCP[v].buffer(0.002))\n",
    "        #plotPoly(triArea)\n",
    "        #plotPoly(countyGeom[c])\n",
    "        print(\"here is the vtd partial for skew = \",xyR,\"in county\",c,\". pop vs target was\",Tpop,int(targetICP))\n",
    "        print(\"muni pop vs target was\",Tpop3,int(targetICP))\n",
    "        #plt.show()\n",
    "        SPS_LISTS[i].append(Tlist)\n",
    "        SPS_POPS[i].append(Tpop)\n",
    "        SPS_LISTS3[i].append(Tlist3)\n",
    "        SPS_POPS3[i].append(Tpop3)  #we'll check legitimacy when we put the three pieces together\n",
    "        SPS_SPLITS[i].append(split3)\n",
    "        \n",
    "nTotal, nGood = 0,0\n",
    "for i0, L0 in enumerate(SPS_LISTS[0]):\n",
    "    for i1,L1 in enumerate(SPS_LISTS[1]):\n",
    "        for i2,L2 in enumerate(SPS_LISTS[2]):\n",
    "            nTotal +=1\n",
    "            currentList = L0 + L1 + L2\n",
    "            currentPop =   SPS_POPS[0][i0] +    SPS_POPS[1][i1] +   SPS_POPS[2][i2]\n",
    "            currentList3 = SPS_LISTS3[0][i0] +  SPS_LISTS3[1][i1] + SPS_LISTS3[2][i2]\n",
    "            currentPop3 =  SPS_POPS3[0][i0] +   SPS_POPS3[1][i1] +  SPS_POPS3[2][i2]\n",
    "            currentSplits = SPS_SPLITS[0][i0] + SPS_SPLITS[1][i1] + SPS_SPLITS[2][i2]\n",
    "            print(\"triArea vtd and muni pops are\",currentPop, currentPop3,\"vs aDP\",aDP)\n",
    "            isAllJiggy = True\n",
    "            for i, pair in enumerate([ [currentList, currentPop], [currentList3, currentPop3] ]):\n",
    "                if not (isContiguous(pair[0],neighborList) and abs(pair[1] - aDP) < toler):\n",
    "                    isAllJiggy = False\n",
    "            if currentSplits > 1:\n",
    "                isAllJiggy = False  #more than one of the partials has a split\n",
    "            if isAllJiggy:\n",
    "                triLists.append(currentList)\n",
    "                triLists3.append(currentList3)\n",
    "                nGood +=1\n",
    "print(\"we created\",nGood,\"out of\",nTotal,\"attempted Summ-Port-Stark districts\")\n",
    "\n",
    "print(\"let us write the SummPortStark schemeA triCounty lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "#date = \"01Apr\"\n",
    "rLISTS = [triLists.copy(), triLists3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo = list(), list()\n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "\n",
    "\n",
    "    #unindent and use for multidistrict plans\n",
    "    #dTL = list()  #tractlist for each district\n",
    "    #dWeight = list()\n",
    "    #dRegionNo = list()\n",
    "    \n",
    "    #for p,plan in enumerate(PLANS):  Use this block for multi-district plans\n",
    "    #    for d in plan:\n",
    "    #        dTL.append(d)\n",
    "    #        #dWeight.append(rWeights[i][p])\n",
    "    #        dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"SummPortStark\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)\n",
    "\n",
    "\n",
    "print(\"A: the below builds Summit 4-district sets for each Summit triArea.  10 tries / triArea, we store any legit 4-district set\")\n",
    "toler = 0.05 * aDP\n",
    "Summ4LISTS, Summ4LISTS3, Summ4TriNo = list(), list(), list()\n",
    "j = 0\n",
    "c = SummPortStark[j] #Summit\n",
    "mC = c #to use Dayton notation\n",
    "for iii, L in enumerate(SummTriLISTS):\n",
    "    mCpool, mCpool3, muni2dPool3, mCpoolPop, mCpoolPop3 = list(), list(), list(), 0., 0.\n",
    "    for v in countyTractList[c] :\n",
    "        if v not in L:\n",
    "            mCpool.append(v)\n",
    "            mCpoolPop += tractPop3[v]\n",
    "        if v not in SummTriLISTS3[iii]:\n",
    "            mCpool3.append(v)\n",
    "            mCpoolPop3 += tractPop3[v]\n",
    "            if tractMuniNo[v] not in muni2dPool3:  #remember, the triArea district didn't have a Summit split\n",
    "                muni2dPool3.append(tractMuniNo[v])  #the muni's not in the straddler HD3 - used later in tryNoSplit 2district\n",
    "                \n",
    "    tgt2Pop =  2. * (mCpoolPop )  / int(countyDistricts[mC])  #\"2.\" for 1st of two orthog pizzacuts\n",
    "    tgt2Pop3 = 2. * (mCpoolPop3) / int(countyDistricts[mC])\n",
    "    maxPop, minPop = 2.05*aDP, 1.95*aDP #narrowing these a bit to increase success of later splitting 2 --> 4\n",
    "    tol2 = r3(max(0.02*aDP,min(maxPop - tgt2Pop , tgt2Pop - minPop)) ) #for simplicity, narrow the range so we won't fail on either side ...\n",
    "    tol3 = r3(max(0.02*aDP, min(maxPop - tgt2Pop3, tgt2Pop3- minPop)) ) # ... but force a positive small tolerance\n",
    "    #print(\"for vtdNo\",t,\"targetPops,(tol) for 2-districts w vtdSnap, muniSnap are\",int(tgt2Pop),\"(\",tol2, int(tgt2Pop3),\"(\",tol3,\")\" )\n",
    "    #print(\" ... based on countyPop, snappedHDpop, HD3pop of\", countyPop[mC], mCsnapPop, mCsnapPop3)\n",
    "\n",
    "    print(\"total 4-district pops for vtd- and muni-snapped should be\",mCpoolPop, mCpoolPop3)\n",
    "\n",
    "    cutPoint = get_PCP(mCpool, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts   \n",
    "    cutPoint3 = get_PCP(mCpool3, tractCP, tractPop3) #pop center of the mC's tracts in the 4 whole districts       \n",
    "    drawUnsuccessful = True\n",
    "    maxTries = 10 #10 #5 #15\n",
    "    nTries = 0\n",
    "    nInPlayTracts = len(mCpool)\n",
    "    sliceAngle = random.uniform(0., 2.*pi)\n",
    "    while nTries < maxTries: #and drawUnsuccessful == True:  #create the four districts triggered by a random initial cut angle ...\n",
    "        drawUnsuccessful = True #reset here b/c we take any legit 4-district set for each Summit triArea partial\n",
    "        tempLISTS, tempLISTS3 = list(), list() #these will house the 4 district tractLists when both vtd- and muni-drawn 4 are Jiggy\n",
    "        halfList, halfList3, halfPop, halfPop3 = [ [],[] ], [ [],[] ], [0.,0.], [0.,0.]\n",
    "        soFarSoGood = True\n",
    "        nTries +=1  #3/7/23 modification - do NOT specify a starter tract, let cutVTDsnap figure it out\n",
    "        needStarterTract = True\n",
    "        #while needStarterTract:\n",
    "        #    mCcT = mCpool[random.randint(nInPlayTracts)] #... based on a random vtd in these 4 districts for BOTH snapping technqs\n",
    "        #    if mCcT in mCpool3:\n",
    "        #        needStarterTract = False\n",
    "        sliceAngle += math.pi / maxTries  #we methodically try a series of possible cut angles.  (Some will lead to discontinuity)\n",
    "        sliceAngle3 = sliceAngle\n",
    "        # = getSliceAngle(tractCP[mCcT], cutPoint, tractCP[mCcT])  #we use same angle for vtd- & muni-snapped\n",
    "        halfList[0], halfPop[0] = cutVtdSnap(tgt2Pop, tol2, tractCP, mCpool, neighborList, tractPop3, sliceAngle, cutPoint)\n",
    "            \n",
    "        halfList3[0], hML, halfPop3[0], splitMuni = cutMuniSnap(tgt2Pop3, tol3, muniTractList,tractCP, mCpool3, muniNeighborList, \n",
    "                                                                tractPop3, muniPop, muniPCP, sliceAngle, cutPoint3)\n",
    "        popGap, fillPop = tgt2Pop3 - halfPop3[0], 0.\n",
    "        if abs(popGap) > tol3 :  #try alternate muni's, do not allow a split at this stage\n",
    "            #print(t,nTries,len(halfList3[0]),halfPop3[0], popGap, int(tol3),\"t,nTries, halfList3[0] len, pop & its popGap, tol3 prior to gFL3nS attempt\")\n",
    "            hT = getFarthestTract(cutPoint3, halfList3[0], tractCP)  #\"home\" tract to bias fill-in toward compactness\n",
    "            vlist, fillPop = getFillList3noSplit(hT, popGap, tol3,aDP, muniPop, muniPCP, muniTractList, muniNeighborList,hML,\n",
    "                                             muni2dPool3, tractCP, neighborList, tractPop3)\n",
    "            halfList3[0] += vlist\n",
    "            halfPop3[0]  += fillPop\n",
    "            #print(\"gFL3nS added pop of\",fillPop,\"from\",len(vlist),\"vtds.  halfList3 len now\",len(halfList3[0]) )\n",
    "        \n",
    "        print(iii,nTries,\"iii,try no. 2-district pops were\",halfPop[0],halfPop3[0],\"vs tgts\",int(tgt2Pop), int(tgt2Pop3)) \n",
    "        for v in mCpool:\n",
    "            if v not in halfList[0]:\n",
    "                halfList[1].append(v)\n",
    "                halfPop[1]  += tractPop3[v]\n",
    "        muniPool3 = [list(), list()]  #the list of munis in each half of the 4-district set (we did not allow splitMuni in prev cut)\n",
    "        for v in mCpool3:\n",
    "            if v not in halfList3[0]:\n",
    "                halfList3[1].append(v)\n",
    "                halfPop3[1] += tractPop3[v]\n",
    "            elif tractMuniNo[v] not in muniPool3[0]:\n",
    "                muniPool3[0].append(tractMuniNo[v])\n",
    "        for m in countyMuniList[mC]:\n",
    "            if m not in muniPool3[0] and muniTractList[m][0] not in SummTriLISTS3[iii]:\n",
    "                muniPool3[1].append(m)\n",
    "        #print(\"and other-slice halfpops were\",halfPop[1], halfPop3[1])\n",
    "        #sIC = 0\n",
    "        #for v in countyTractList[mC]:\n",
    "        #    if v in HDtractList3[t]:\n",
    "        #        sIC +=1\n",
    "        #print(\"nVTDs in half-munisnap0, half-munisnap1, straddler-in-county, total county are\", len(halfList3[0]), len(halfList3[1]),sIC,len(countyTractList[mC]))\n",
    "        for v in countyTractList[mC]:\n",
    "            if v in halfList3[0] and v in halfList3[1]:\n",
    "                raise Exception(v,\"is in both halfLists for\",iii,nTries)\n",
    "            if v in halfList3[1] and v in SummTriLISTS3[iii]:\n",
    "                raise Exception(v,\"is in 2nd HL3 and straddler for\",iii,nTries)\n",
    "            if v in halfList3[0] and v in SummTriLISTS3[iii]:\n",
    "                raise Exception(v,\"is in 1st HL3 and straddler for\",iii,nTries)\n",
    "        \n",
    "        if not ( isContiguous(halfList[1],neighborList) and isContiguous(halfList3[1],neighborList) ) :\n",
    "            soFarSoGood = False\n",
    "            print(\"ABORT for iii,angle deg\",iii,int(180./3.1416*sliceAngle),\"one of the 2-district pieces is discontiguous\")\n",
    "            continue  #try a new angle; early discontiguity\n",
    "        if max(abs(halfPop[0]-2*aDP),abs(halfPop[1]-2*aDP),abs(halfPop3[0]-2*aDP), abs(halfPop3[1]-2*aDP) ) > 0.08 * aDP:\n",
    "            soFarSoGood = False\n",
    "            print(\"ABORT for iii,angle deg=\",iii, int(180./3.1416*sliceAngle),\" - our halfPops or halfPop3s are too far from 2*aDP\")\n",
    "            continue #try a new angle; unlikely to hit pop range for qtr districts\n",
    "            \n",
    "        #OK, both 2-district sets are good.  Now try some angles for splitting each of these into \"qtr\" districts\n",
    "        qtrList, qtrList3, qtrPop, qtrPop3 = [list(),list(),list(),list()], [list(),list(),list(),list()], [0.]*4, [0.]*4\n",
    "        nGoodQtrCuts = 0\n",
    "        vtdAngle = [0.]*2\n",
    "        for i, HL in enumerate(halfList):  #first, attempt to generate four good qtr districts for vtd-snap\n",
    "            off2Pop = abs(0.5*halfPop[i] - aDP)\n",
    "            TOL = max( 0.02*aDP, 0.05*aDP - 0.5*off2Pop)  #tighten if 2-district off\n",
    "            qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            qtrCutNo = random.randint(maxQtrCutNo) #we'll make four 45deg-spaced cut tries, but randomize which to try first\n",
    "            \n",
    "            while qLoopNo < maxQtrCutNo: #and haveCutGoodQtr; make all 4 possiblel cuts, keep trying for better\n",
    "                qAngle = sliceAngle + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo #0deg, 45deg, 90deg, 135deg vs orig cut\n",
    "                qElist, qtrPop[qE] = cutVtdSnap(0.5*halfPop[i], TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)  \n",
    "                qtrPop[qO] = halfPop[i] - qtrPop[qE]\n",
    "                qOlist = list()\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop[qE] - aDP) <= 0.05*aDP and abs(qtrPop[qO] - aDP) <= 0.05*aDP :  \n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList[qE], qtrList[qO] = qElist.copy(), qOlist.copy()\n",
    "                            vtdAngle[i] = qAngle #to preserve same angle for 1st try in later munisnap 4-district set\n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        if nGoodQtrCuts < 2:  #we failed to make four contiguous districts for vtd-snap case.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty vtd-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            #plotPoly(countyGeom[mC])\n",
    "            #plotPoly(stradPoly)\n",
    "            #for q in qtrList:\n",
    "            #    if not isContiguous(q,neighborList):\n",
    "            #        for v in q:\n",
    "            #            plotPoly(tractGeom[v])\n",
    "            #plt.show()\n",
    "            continue  #don't bother quartering up the muni-snap\n",
    "            \n",
    "        for i, HL in enumerate(halfList3):  #... and now, 4 qtr-districts for muni-snap\n",
    "            qCutPoint3  = get_PCP(HL, tractCP, tractPop3)\n",
    "            qE, qO = int(2*i), int(2*i+1)  #even and odd indices for the quarter districts\n",
    "            off2Pop3 = abs(0.5*halfPop3[i] - aDP)\n",
    "            TOL3 = max( 0.02 * aDP, toler - 0.5*off2Pop3)  #tighten if 2-district off\n",
    "            qtrCutNo ,maxQtrCutNo, cutGoodQtr = 0, 4, False\n",
    "            lowScore = 999999.\n",
    "            low_qElist, low_qOlist = list(), list()  #most-compact-so-far pair of qtr districts for this halfList\n",
    "            qLoopNo, maxQtrCutNo, haveCutGoodQtr, justCutGoodQtr = 0, 4, False, False\n",
    "            qLoopNo = 0\n",
    "            sliceAngle3 = vtdAngle[i]  #of four try angles, we start at the angle that worked for vtdSnap \n",
    "            while qLoopNo < maxQtrCutNo : #and not cutGoodQtr :\n",
    "                qAngle = sliceAngle3 + math.pi * float(qLoopNo + qtrCutNo) / maxQtrCutNo               \n",
    "                #Polly = angledPentaPoly(cutPoint3, qAngle, 5.*tractCP[mCfT].distance(cutPoint3) )\n",
    "                #if Polly.disjoint(tractCP[mCfT]):\n",
    "                #    qAngle += math.pi\n",
    "                qElist, qML, qtrPop3[qE], splitMuni = cutMuniSnap(0.5*halfPop3[i], TOL3, muniTractList,tractCP, HL,\n",
    "                                                                      muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "                popGap = 0.5*halfPop3[i] - qtrPop3[qE]\n",
    "                list3B, pop3B = list(),0.\n",
    "                if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "                    hT = getFarthestTract(cutPoint3, qElist, tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "                    list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,qML,\n",
    "                                                              muniPool3[i],tractCP, neighborList,tractPop3,0)\n",
    "                qElist, qtrPop3[qE] = qElist + list3B, qtrPop3[qE] + pop3B\n",
    "                qtrPop3[qO] = halfPop3[i] - qtrPop3[qE]\n",
    "                qOlist = list()\n",
    "                #print(\"t,i\",t,i, qtrPop[qE],qtrPop[qO],\"<-qpops, q3pops -->\",qtrPop3[qE],qtrPop3[qO])  #debug\n",
    "                for tt in HL:\n",
    "                    if tt not in qElist:  #throw all tracts not in 0th (or 2nd) whole district into the 1st (or 3rd)\n",
    "                        qOlist.append(tt)\n",
    "                if abs(qtrPop3[qE] - aDP) <= 0.05*aDP and abs(qtrPop3[qO] - aDP) <= 0.05*aDP :\n",
    "                    if isContiguous(qElist, neighborList) and isContiguous(qOlist, neighborList):\n",
    "                        haveCutGoodQtr, justCutGoodQtr = True, True\n",
    "                        score = compactScore(qElist,tractCP, tractPop3) + compactScore(qOlist, tractCP,tractPop3)\n",
    "                        if score < lowScore:  #this is the most compact 2d set, move to leaderboard\n",
    "                            lowScore = score\n",
    "                            qtrList3[qE], qtrList3[qO] = qElist.copy(), qOlist.copy()               \n",
    "                qLoopNo +=1\n",
    "            if haveCutGoodQtr:\n",
    "                nGoodQtrCuts +=1\n",
    "                \n",
    "        #print(\"t,tryNo, final dPops for vtd- and muni\",t,nTries,qtrPop,qtrPop3)\n",
    "        if nGoodQtrCuts < 4:  #we failed to make four contiguous districts for both cases.  Must try another original angle\n",
    "            #print(\"We failed to make four contiguous inCounty muni-snapped districts for tract\",t,\"on try\",nTries)\n",
    "            soFarSoGood = False\n",
    "            continue\n",
    "        \n",
    "        if soFarSoGood:\n",
    "            drawUnsuccessful = False\n",
    "            print(t,\"has drawn a GOOD 4-district Summit for both vtd- and muni-snapped on try\",nTries,\"for triDistrict\",iii)\n",
    "            for qD in range(4):\n",
    "                tempLISTS.append(qtrList[qD])\n",
    "                tempLISTS3.append(qtrList3[qD])\n",
    "            Summ4LISTS.append(tempLISTS)\n",
    "            Summ4LISTS3.append(tempLISTS3)\n",
    "            Summ4TriNo.append(iii)  #the iii points back to an index in the list of triArea districts\n",
    "print(\"we drew a TOTAL of\",len(Summ4LISTS),\"legit 4-district Summit sets out of\",maxTries,\"x\",len(SummTriLISTS) )\n",
    "\n",
    "\n",
    "print(\"let us write the Summit 4A whole-district lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "date = \"01Apr\"\n",
    "rLISTS = [Summ4LISTS.copy(), Summ4LISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "pointerList = Summ4TriNo.copy()\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    triPointer = list() \n",
    "    #dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    planNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            #dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            planNo.append(p)\n",
    "            triPointer.append(pointerList[p])    \n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"planNo\":planNo,\"triCounty pointer\":triPointer,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"Summ4A\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)\n",
    "\n",
    "\n",
    "print(\"A: before we forget -- move the remainder Portage pop into a full district\")\n",
    "PortLISTS, PortLISTS3, PortTriNo = list(), list(), list()\n",
    "j = 1\n",
    "c = SummPortStark[j]  #Portage\n",
    "for i, L in enumerate(SPS_LISTS[j]):   \n",
    "    remnList, remnList3, remnPop, remnPop3 = list(), list(), 0., 0.\n",
    "    for v in countyTractList[c] :\n",
    "        if v not in L:\n",
    "            remnList.append(v)\n",
    "            remnPop += tractPop3[v]\n",
    "        if v not in SPS_LISTS3[j][i]:\n",
    "            remnList3.append(v)\n",
    "            remnPop3 += tractPop3[v]\n",
    "    isJiggy = True\n",
    "    if max(abs(remnPop - aDP),abs(remnPop3 - aDP) ) > 0.05*aDP:\n",
    "        isJiggy = False\n",
    "        print(\"Uh oh, our remnant Portage pop for Portage remnant\",i,\"was\",remnPop, remnPop3,\"vs range\",int(0.95*aDP),\"-\",int(1.05*aDP) )\n",
    "    if not isContiguous(remnList3,neighborList) :\n",
    "        isJiggy = False\n",
    "        print(\"Uh oh, our remnant Portage pop for Portage remnant\",i,\"was discontiguous\" )\n",
    "        for v in remnList3:\n",
    "            plotPoly(tractGeom[v])\n",
    "        plotPoly(countyGeom[c])\n",
    "        plt.show()\n",
    "    if isJiggy:\n",
    "        PortLISTS.append(remnList)\n",
    "        PortLISTS3.append(remnList3)\n",
    "        PortTriNo.append(i)   #the pointer back to the tri-County Portage partial district that triggered this Portage remnant\n",
    "print(\"we created\",len(PortTriNo),\"Portage remnants (not in the triCounty district).  We hoped to create\",len(SPS_LISTS[j]) )\n",
    "\n",
    "\n",
    "print(\"let us write the Portage optionA whole-district lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "#date = \"01Apr\"\n",
    "rLISTS = [PortLISTS.copy(), PortLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLAN_LISTS in enumerate(rLISTS):  \n",
    "    dTL, dRegionNo = list(), list()\n",
    "    triPointer = list() \n",
    "    for j, DISTRICT_LIST in enumerate(PLAN_LISTS): #use this block for single-district lists not stored in brackets\n",
    "        dRegionNo.append(currentRegionNo)\n",
    "        dTL.append(DISTRICT_LIST)\n",
    "        triPointer.append(PortTriNo[j])\n",
    "\n",
    "    #unindent and use for multidistrict plans\n",
    "    #dTL = list()  #tractlist for each district\n",
    "    #dWeight = list()\n",
    "    #dRegionNo = list()\n",
    "    \n",
    "    #for p,plan in enumerate(PLANS):  Use this block for multi-district plans\n",
    "    #    for d in plan:\n",
    "    #        dTL.append(d)\n",
    "    #        #dWeight.append(rWeights[i][p])\n",
    "    #        dRegionNo.append(currentRegionNo)\n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"triCounty pointer\":triPointer,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"PortageA\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)\n",
    "\n",
    "\n",
    "print(\"A:now, slice Stark (c=75) into 3 whole pieces.  Cut east on various angles, then bisect remainder\")\n",
    "forcedMunis = [[318],[],[] ] #force the partial of Stark's NE district to scoop up all remaining of 318 not in the triDistrict\n",
    "nStarkSplits = [1, 0, 0]     #and this pickup counts as a split in this inCounty district\n",
    "finalStarkLISTS, finalStarkLISTS3, finalStarkAngles = list(), list(),list()\n",
    "Stark3TriNo = list()  #pointer back to the Stark triCounty partial that triggered this 3-district Stark draw\n",
    "minAngle, maxAngle, nAngles = 0.5 * math.pi, 0.8 * math.pi, 5\n",
    "j = 2\n",
    "c = SummPortStark[j]  #Stark County\n",
    "nStarkFail, nStarkGood = 0,0\n",
    "for iii, L in enumerate(SPS_LISTS[j]):\n",
    "    remnList, remnList3, remnPop, remnPop3 = list(), list(), 0., 0.\n",
    "    bannedVtdList = list()\n",
    "    for v in countyTractList[c] :\n",
    "        if v not in L:\n",
    "            remnList.append(v)\n",
    "            remnPop += tractPop3[v]\n",
    "        if v not in triLists3[iii]:\n",
    "            remnList3.append(v)\n",
    "            remnPop3 += tractPop3[v]\n",
    "    CaDP =  remnPop  / int(countyPop[c]/aDP)  #shade targets and tolerances to maximize chance of all three good districts\n",
    "    CaDP3 = remnPop3 / int(countyPop[c]/aDP)\n",
    "    Ctoler, Ctoler3 = min(CaDP - 0.95*aDP, 1.05*aDP - CaDP) , min(CaDP3 - 0.95*aDP, 1.05*aDP - CaDP3)\n",
    "    for angleNo in range(nAngles):\n",
    "        angle = minAngle + (maxAngle - minAngle) * angleNo/ float(nAngles - 1)\n",
    "        print(\"now trying angle\",angleNo,\"of\",nAngles,\"angle deg =\",r3(angle*180./3.1416),\"for triArea district\",iii )\n",
    "        StarkLISTS, StarkPops   = [list(), list(), list()], [0., 0., 0.]\n",
    "        StarkLISTS3, StarkPops3 = [list(), list(), list()], [0., 0., 0.]\n",
    "        NEcorner = getClosestTract(Point(-81.08, 40.95), remnList, tractCP)  #ID the Stark NE corner tract to force it into Stark D1\n",
    "        if angledPentaPoly(countyPCP[c], angle, 5.*countyGeom[c].area**0.5 ).disjoint(tractCP[NEcorner]) :\n",
    "            angle += math.pi\n",
    "        StarkLISTS[0], StarkPops[0] = cutVtdSnap(CaDP, Ctoler, tractCP, remnList,neighborList,tractPop3, angle, countyPCP[c], NEcorner)\n",
    "        Stark0List3 = remnList3.copy()\n",
    "        forcedList3, forcedPop3 = list(), 0.\n",
    "        for v in countyTractList[c]:\n",
    "            if tractMuniNo[v] in forcedMunis[0] and v in remnList3:  #we don't let the NE Stark district pick up the banned triArea remnant\n",
    "                Stark0List3.remove(v)\n",
    "                forcedList3.append(v)\n",
    "                forcedPop3 += tractPop[v]\n",
    "        aDP0 = CaDP3 - forcedPop3  #the target pop in the 0th in-Stark district, not including the forced pop from muni 318's remnant\n",
    "        list3, cML3, pop3, splitMuni = cutMuniSnap(aDP0, Ctoler3, muniTractList, tractCP, Stark0List3, muniNeighborList,tractPop3,\n",
    "                                                   muniPop, muniPCP, angle, countyPCP[c])\n",
    "        popGap = aDP0 - pop3\n",
    "        list3B, pop3B = list(), 0.\n",
    "        if (abs(popGap) > Ctoler3) : #we stopped short of popTgt; adding next muni would put mainCounty iCP too far above target\n",
    "            if popGap < 0  :\n",
    "                raise Exception(\"error! pop3 > targetICP, which means we overadded muni's.  STOP\")\n",
    "            cMLremn = countyMuniList[c].copy()\n",
    "            for m in forcedMunis[0]:\n",
    "                if m in countyMuniList[c]:\n",
    "                    cMLremn.remove(m)  #banning the double-pickup of 318's partial\n",
    "            list3B, pop3B = getFillList3noSplit(NEcorner, popGap, Ctoler3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,\n",
    "                                                cML3, cMLremn,tractCP, neighborList,tractPop3)\n",
    "            #not allowing a split because we are absorbing 318's partial\n",
    "            if pop3B == 0. :\n",
    "                print(\"uh oh, failed to muni-snap in county\",c,\"with angle\",angle,\"we are short by\",popGap)\n",
    "        \n",
    "        StarkLISTS3[0], StarkPops3[0] = forcedList3 + list3 + list3B, forcedPop3 + pop3 + pop3B  #this is the E / NE Stark whole district.\n",
    "        if not (isContiguous(StarkLISTS[0],neighborList) and isContiguous(StarkLISTS[0],neighborList)) :\n",
    "            print(r3(180./math.pi),\"deg cut for Stark0 created a discontiguous vtd- or muni NE Stark district.  Give up on this HD\")\n",
    "            nStarkFail +=1\n",
    "            continue          \n",
    "        #below - Now bisect remainder.  modified from Lorain\n",
    "        doubleList, doubleList3, doublePop, doublePop3 = list(), list(), 0., 0.\n",
    "        for v in remnList:\n",
    "            if v not in StarkLISTS[0]:\n",
    "                doubleList.append(v)\n",
    "                doublePop  += tractPop3[v]\n",
    "        muniPool3 = list()  #the list of munis in remaining 2-district set (we did not allow splitMuni in prev cut)\n",
    "        for v in remnList3:\n",
    "            if v not in StarkLISTS3[0] :\n",
    "                doubleList3.append(v)\n",
    "                doublePop3 += tractPop3[v]\n",
    "                if tractMuniNo[v] not in muniPool3:  #triDistrict + Stark district0 do not bequeath any split muni's to d1 or d2\n",
    "                    muniPool3.append(tractMuniNo[v])\n",
    "        if not (isContiguous(doubleList,neighborList) and isContiguous(doubleList3,neighborList)) :\n",
    "            print(r3(180./math.pi),\"deg cut for Stark0 left a discontiguous vtd- or muni 2-district remnant.  Give up on this HD\")\n",
    "            nStarkFail +=1\n",
    "            continue\n",
    "\n",
    "        #for i, HL in enumerate(halfList):  #revised Dayton code -- here, only one more cut to make, not 2 halves\n",
    "        HL = doubleList.copy()  #reusing Dayton nomenclature\n",
    "        off2Pop = abs(0.5*doublePop - aDP)\n",
    "        TOL = max( 0.02*aDP, 0.05*aDP - 0.5*off2Pop)  #tighten if 2-district off\n",
    "        qCutPoint  = get_PCP(HL, tractCP, tractPop3)\n",
    "        minAngle2, maxAngle2 = 0., math.pi,   \n",
    "        qAngle0 = random.uniform(minAngle, maxAngle)  #any cut may work\n",
    "\n",
    "        bestDlists = [list(), list(), list(), list()]\n",
    "        nAngleTries,lowScore = 10, 999999.\n",
    "        qLoopNo = 0\n",
    "        foundGoodAngle = False\n",
    "        while qLoopNo < nAngleTries:  #try multiple angles, save the districts with lowest score if all districts legit\n",
    "            qLoopNo +=1\n",
    "            qAngle = qAngle0 + math.pi * float(qLoopNo)/nAngleTries\n",
    "            #print(\"trying angle\",qAngle*180./3.1416,\"for 1st cut angle\",angle*180./3.1416)\n",
    "            dblList, dblPop = [list(), list()], [0., 0.]\n",
    "            dblList[0], dblPop[0] = cutVtdSnap(0.5*doublePop, TOL, tractCP, HL, neighborList, tractPop3, qAngle, qCutPoint)\n",
    "            for tt in HL:\n",
    "                if tt not in dblList[0]:  #throw all remaining vtds into 2nd whole Lorain district\n",
    "                    dblList[1].append(tt)\n",
    "            dblPop[1] = doublePop - dblPop[0]\n",
    "\n",
    "            HL3 = doubleList3.copy()  #reusing Dayton code\n",
    "            dblList3, dblPop3 = [list(), list()], [0., 0.]\n",
    "            qCutPoint3 = get_PCP(HL3, tractCP, tractPop3)  #note: reusing same angle as vtd-snapped (horiz,diag or vert)\n",
    "            off2Pop3 = abs(0.5*doublePop3 - aDP)\n",
    "            TOL3 = max( 0.02*aDP, toler - 0.5*off2Pop3)  #tighten if 2-district off\n",
    "            dblList3[0], dML, dblPop3[0], splitMuni = cutMuniSnap(0.5*doublePop3, TOL3, muniTractList,tractCP, HL3,\n",
    "                                                                  muniNeighborList,tractPop3, muniPop, muniPCP, qAngle, qCutPoint3)\n",
    "            popGap = 0.5*doublePop3 - dblPop3[0]\n",
    "            list3B, pop3B = list(),0.\n",
    "            if abs(popGap) > TOL3 :  #would like to avoid a split, but OK if we have one in these pair of districts\n",
    "                hT = getFarthestTract(qCutPoint3, dblList3[0], tractCP)  #\"home\" tract for ID'ing addl muni's to work into qtr district\n",
    "                list3B, pop3B, nSplits = tryNoSplit(hT, popGap, TOL3, aDP, muniPop, muniPCP, muniTractList, muniNeighborList,dML,\n",
    "                                                          muniPool3,tractCP, neighborList,tractPop3,0)\n",
    "            dblList3[0], dblPop3[0] = dblList3[0] + list3B, dblPop3[0] + pop3B\n",
    "            for tt in HL3:\n",
    "                if tt not in dblList3[0]:  #throw all remaining vtds into 3rd whole Lucas district\n",
    "                    dblList3[1].append(tt)\n",
    "            dblPop3[1] = doublePop3 - dblPop3[0]\n",
    "            isJiggy = True\n",
    "            score = 0.\n",
    "            currentLists,currentPops = [dblList[0], dblList[1], dblList3[0], dblList3[1]] ,[dblPop[0],dblPop[1], dblPop3[0], dblPop3[1] ]\n",
    "            for L in currentLists:\n",
    "                score += compactScore(L,tractCP, tractPop3)\n",
    "                if not isContiguous(L, neighborList):\n",
    "                    isJiggy = False\n",
    "            for p in currentPops :\n",
    "                if abs(p - aDP) > toler:\n",
    "                    isJiggy = False\n",
    "            if isJiggy and score < lowScore:\n",
    "                foundGoodAngle = True\n",
    "                lowScore = score\n",
    "                bestDlists = currentLists.copy()\n",
    "                bestDpops =  currentPops.copy()\n",
    "                bestAngle = qAngle\n",
    "\n",
    "        if foundGoodAngle:  #overwrite.  If we didn't overwrite, we'll catch the fails in the nGood block\n",
    "            dblList[0], dblList[1], dblPop[0], dblPop[1] = bestDlists[0], bestDlists[1], bestDpops[0], bestDpops[1]\n",
    "            dblList3[0], dblList3[1], dblPop3[0], dblPop3[1] = bestDlists[2], bestDlists[3], bestDpops[2], bestDpops[3]\n",
    "        for i, L in enumerate(dblList3):\n",
    "            StarkLISTS[i+1] = dblList[i]\n",
    "            StarkLISTS3[i+1] = dblList3[i]\n",
    "            StarkPops[i+1] = dblPop[i]\n",
    "            StarkPops3[i+1] = dblPop3[i]\n",
    "        nGood, nGood3 = 0,0\n",
    "        for i, L in enumerate(StarkLISTS):\n",
    "            if abs(StarkPops[i] - aDP) <= 0.05 * aDP and isContiguous(StarkLISTS[i],neighborList):\n",
    "                nGood +=1\n",
    "            else:\n",
    "                print(t,i,StarkPops[i],isContiguous(StarkLISTS[i],neighborList),\"t,i,vtd-pop,contiguity = FAIL\")\n",
    "            if abs(StarkPops3[i] - aDP) <= 0.05 * aDP and isContiguous(StarkLISTS3[i],neighborList):\n",
    "                nGood3 +=1\n",
    "            else:\n",
    "                print(t,i,StarkPops3[i],isContiguous(StarkLISTS3[i],neighborList),\"t,i,muni-pop,contiguity = FAIL\")\n",
    "\n",
    "        if nGood == 3 and nGood3 == 3:\n",
    "            nStarkGood +=1\n",
    "            finalStarkLISTS.append( [StarkLISTS[0],  StarkLISTS[1],   StarkLISTS[2] ] )\n",
    "            finalStarkLISTS3.append([StarkLISTS3[0], StarkLISTS3[1],  StarkLISTS3[2] ] )\n",
    "            finalStarkAngles.append( [angle,bestAngle] )\n",
    "            Stark3TriNo.append(iii)\n",
    "        else:\n",
    "            nStarkFail +=1\n",
    "print(\"all done attempting 3 Stark districts.  We had a total of\",nStarkGood,\"successes and\",nStarkFail,\"fails\")\n",
    "\n",
    "print(\"let us write the Stark 3A whole-district lists to a file.  We will vote later\" )    \n",
    "#for rNo in range(nRegions):  #find which region has this county in it\n",
    "#    if mC in regionCountyList[rNo]:\n",
    "#        currentRegionNo = rNo\n",
    "date = \"01Apr\"\n",
    "rLISTS = [finalStarkLISTS.copy(), finalStarkLISTS3.copy()]  #rWeights = [LorainWeights.copy(), LorainWeights3.copy()]\n",
    "pointerList = Stark3TriNo.copy()\n",
    "rType = [\"vtd\",\"muni\"]\n",
    "for i, PLANS in enumerate(rLISTS):\n",
    "    dTL = list()  #tractlist for each district\n",
    "    triPointer = list() \n",
    "    #dWeight = list()\n",
    "    dRegionNo = list()\n",
    "    planNo = list()\n",
    "    for p,plan in enumerate(PLANS):\n",
    "        for d in plan:\n",
    "            dTL.append(d)\n",
    "            #dWeight.append(rWeights[i][p])\n",
    "            dRegionNo.append(currentRegionNo)\n",
    "            planNo.append(p)\n",
    "            triPointer.append(pointerList[p])    \n",
    "\n",
    "    df = pd.DataFrame( {\"regionNo\":dRegionNo,\"planNo\":planNo,\"triCounty pointer\":triPointer,\"HDtractList\":dTL} ) \n",
    "    outname = STATE+date+\"Stark3A\"+\"_\"+rType[i]+\".csv\"\n",
    "    outpath = \"state_HD_output/\"+outname\n",
    "    df.to_csv(outpath)\n",
    "\n",
    "\n",
    "print(\"A: OK, the options for Stark look good, though a couple muni-snapped have queen adjacencies in SW corner\")\n",
    "for i,L in enumerate(finalStarkLISTS[13]):  #chg 13 to other numbers\n",
    "    for v in L:\n",
    "        if i == 0:\n",
    "            plotPoly(tractCP[v].buffer(0.001))\n",
    "        if i == 1:\n",
    "            plotPoly(tractGeom[v])\n",
    "        if i == 2:\n",
    "            plotPoly(tractCP[v].buffer(0.002))\n",
    "plotPoly(countyGeom[c])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d97ca629-adc8-4737-82fe-49d81530fc5b",
   "metadata": {},
   "source": [
    "#10/19/22 - current thinking on regional breakouts.  YOU ONLY VOTE ON OTHER DISTRICTS that are IN-COUNTY AND CONSTRAINED (1.x, 2.x, Lucas 3.x)\n",
    "#*** UNLESS SPECIFIED OTHERWISE, Non-straddler HDs pick the pie cut (of all straddler-generated plans)\n",
    "# ...that minimizes their distance to their district's geographic centroid\n",
    "# ** UNLESS SPECIFIED -- free-range counties fill themselves first, then only pull from their immediate neighbors\n",
    "#   NEW THINKING 10/23/22 - In each region, create a number of regional plans triggered by each mainCounty straddler\n",
    "# Every tract in a main or ppC county picks the regional plan that minimizes their distance to their district's POPULATION centroid\n",
    "# Tracts in non-ppC paired counties only vote if a regional plan centers them better than being paired with another rural neighbor county\n",
    "# For each pre-21 election data set, the regional Dem-seat expectation is a weighted avg across all regional plans (smeared for frac seat in each plan).\n",
    "# The weighting is the fraction of voting pop that preferred that plan\n",
    "\n",
    "#Toledo area: all who are pulled into a 5-district area vote on the 5-district plan (3 Lucas, 1 Wood, 1 straddler)\n",
    "# Lucas straddlers: based on max Ottawa + Henry pop.  SW Lucas with Wood 0.11 + Fulton or E Lucas with Wood 0.11 + Ottawa\n",
    "# Wood: straddlers are the 0.11 / 1.11 ensnared by Lucas-centered HDs with the most Lucas pop\n",
    "#  Remainder of Lucas pie-cut into three.  Two lines, avg of vertical and (perpendicular to remnant tractCP's beeline to Lucas GCP)\n",
    "# non-remnant Lucas and Wood pick plan with a district that has it closest to a GCP\n",
    "# Fulton and Ottawa HDs include 0.63 Lucas, 0.11 Wood if their PolyHD had > 0.63/2 Lucas.\n",
    "# They include 0.11 Wood if their PolyHD had > 0.11 / 2 Wood.    Otherwise, free-range excluding these two\n",
    "\n",
    "#Athens area - all snapped HDs start with a full home county, then add whole counties in decreasing fraction of HDPoly use\n",
    "  until need to add a partial to complete a district.\n",
    "  Generate snapped HDs for 1 aDP worth of Athens-area tracts using this process, then this aDP set each selects a single\n",
    "  district / regional plan that best centers it.  Some choices will have full Athens, some partial, some none.\n",
    "\n",
    "#Franklin - 11 whole\n",
    "#Delaware: 0.8 / 1.8 are straddlers based on Union+Marion+Morrow pop.  Pie-cut the adjoining county.\n",
    "# Knox and Licking ARE PAIRED like Medina and Ashland.  pie-cut along horizontal until 2 districts evened up (angle NOT t-specific)\n",
    "# Fairfield 0.33 / 1.33 could be straddlers based on Pickaway+Hocking+Perry pop, but all red so ignore.\n",
    "# Madison / Fayette / Pickaway are free-range\n",
    "\n",
    "#Lorain county:  ID 0.63 / 2.63 as straddlers based on highest Erie + Huron pop.  Pair with 0.37 E or H based on E vs. H pop in HDPoly.\n",
    "# Each straddler generates a pie cut with an angle halfway between vertical and perpendicular to the tract - Lorain centroid.\n",
    "# Erie and Huron HDs include 0.63 Lorain if there is an HD with 0.63 that centers them better than Erie+Huron PCP.\n",
    "\n",
    "#Dayton area: start with Butler County.  Select the HDs with the highest Preble + Montgomery + Darke pop as the 0.28 remnant\n",
    "#  grow them until they capture all Preble or 0.51 of Montgomery.  If Preble, then complete with Darke partial.  If Mtgmry, then Preble partial\n",
    "#  Butler remnants do not get a \"vote\" on other districts\n",
    "#  Montgomery tracts to be ranked by non(Montgomery+Warren+Darke) population.  Top 0.51 / 4.51 will center straddling districts\n",
    "#  For straddling Montgomery tracts, ID the county with highest population use.\n",
    "#    If Butler or Preble:  0.28 Butler + 0.21 Preble; if Greene or Clark, 0.50 Montgomery + 0.38 Greene + 0.12 Clark.  If Miami, 0.49 Miami\n",
    "#  Dayton-area voting: similar to Lorain area.  Plan voting on \n",
    "#  Any tract drawn into a Montgomery-containing district can choose a straddling district if that centers them better than their default non-Montgomery district\n",
    "#     Remnant Montgomery HDs do not vote on full Montgomery district plan\n",
    "#     Compute population center of each remnant HD\n",
    "#   Non-remnant Montgomery HDs: pick the remnant with pop centroid farthest away.  Make a wedge on non-remnant Montgomery centroid to deliver aDP\n",
    "#   Non-remnant Butler HDs:    Same as Montgomery\n",
    "#  Preble:  The 0.21 / 0.34 with highest Dayton + Butler pop will center straddling districts.  Others will be full Darke + Miami partial\n",
    "#  Darke:  all Darke + 0.13 Preble + free-range\n",
    "#  Miami: All Miami.  For each HD, pick county that HDPoly pulls most from out of Darke, Shelby, Champaign, Clark.  If Clark, take 0.11, not 0.09\n",
    "#  Clark: 0.14 / 1.14 with most noncounty pop become straddlers.  (Montg+Greene, or Miami 1.03 aDP, or NE free range. These vote on whole Clark. \n",
    "#    Whole Clarks' pick the farthest remnant\n",
    "#  Greene: 0.41 / 1.41 are straddlers based on nonGreene, nonWarren pop.  Greene straddlers vote on whole Greene\n",
    "#     Greene straddlers pair with 0.13 Clark + 0.48 Montgomery, or Madison+Fayette or Clinton+Fayette\n",
    "\n",
    "\n",
    "\n",
    "#THRESHOLD FOR PICKING PRIMARY PAIRING COUNTY FOR A STRADDLING HD: higher of pairing A pop / countyA pop vs. pairing B pop / countyB pop  ??  total pop?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d786006-5209-4b92-b17c-c61485df5001",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fd44f142-6fab-4b8c-a383-23dcb26be052",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For each whole county, we'll now compare the HD-average county lean to the true county lean\n",
      "for county 24 with 11.0 districts, true pct Trumpers = 0.3405 vs HDavg 0.33497\n",
      "for county 30 with 7.0 districts, true pct Trumpers = 0.41939 vs HDavg 0.41524\n",
      "for county 42 with 2.0 districts, true pct Trumpers = 0.56896 vs HDavg 0.56663\n",
      "for county 49 with 2.0 districts, true pct Trumpers = 0.50962 vs HDavg 0.49351\n",
      "for county 69 with 1.0 districts, true pct Trumpers = 0.70158 vs HDavg 0.70158\n",
      "for county 82 with 2.0 districts, true pct Trumpers = 0.65635 vs HDavg 0.65247\n",
      "for county 84 with 1.0 districts, true pct Trumpers = 0.68813 vs HDavg 0.68813\n"
     ]
    }
   ],
   "source": [
    "print(\"For each whole county, we'll now compare the HD-average county lean to the true county lean\")\n",
    "for c in range(nCounties):\n",
    "    if isWholeCounty[c] == 1:\n",
    "        countyHD_Trumpers = 0.\n",
    "        countyHDvoters = 0.\n",
    "        for t in countyTractList[c]:\n",
    "            for tt in HDtractList[t]:\n",
    "                countyHD_Trumpers += tractPop3[t] / countyPop[c]* vtdTrump[tt]\n",
    "                countyHDvoters += tractPop3[t] / countyPop[c]* (vtdTrump[tt] + vtdBiden[tt])\n",
    "        countyHDvGOP = countyHD_Trumpers / countyHDvoters\n",
    "        cD = round(countyDistricts[c],0)\n",
    "        print(\"for county\",c,\"with\",cD,\"districts, true pct Trumpers =\",r5(county_vGOP[c]),\"vs HDavg\",r5(countyHDvGOP))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4206726a-2e28-4e15-833c-be16fe22ca64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "now, we will generate a final list of districts including single and regional plans\n",
      "For single HDs (in wholeDistrict counties), seat weight = tractPop / county avgDistrictPop\n",
      "For regional plans, seat weights reflect the fraction of region supporting that plan\n"
     ]
    }
   ],
   "source": [
    "print(\"now, we will generate a final list of districts including single and regional plans\")\n",
    "print(\"For single HDs (in wholeDistrict counties), seat weight = tractPop / county avgDistrictPop\")\n",
    "print(\"For regional plans, seat weights reflect the fraction of region supporting that plan\")\n",
    "need2read = \"yes\"\n",
    "if need2read == \"yes\":  #cold re-start\n",
    "    import ast\n",
    "    infilename = \"OH99nocountysplit_regionLists.csv\"\n",
    "    regionListFile = pd.read_csv(\"state_HD_output/nocountysplit2process/\"+infilename)\n",
    "    infilename = \"OH99nocountysplit_districtTractLists.csv\"\n",
    "    districtListFile = pd.read_csv(\"state_HD_output/nocountysplit2process/\"+infilename)\n",
    "    infilename = \"OH99nocountysplit_HDtractLists.csv\"\n",
    "    HDTLFile = pd.read_csv(\"state_HD_output/nocountysplit2process/\"+infilename)\n",
    "    chosenPlan = HDTLFile['chosenPlan']\n",
    "    tractPop = HDTLFile['tractPop']\n",
    "    countyNo = HDTLFile['countyNo']\n",
    "    HDTL = HDTLFile['HDtractList']\n",
    "    PLANNO = regionListFile['PLANNO']\n",
    "    planRegionNo = regionListFile['planRegionNo']\n",
    "    planTLnosList = regionListFile['planTLnos']         #need to ast this list; see below\n",
    "    planChoosersList = regionListFile['planChoosers']   #need to ast this list; see beo\n",
    "    DTLNO = districtListFile['DTLNO']\n",
    "    districtTractListString = districtListFile['districtTractList']\n",
    "    # for two of these lists, we need to convert from string to actual lists\n",
    "    nPlans = len(PLANNO)\n",
    "    planTLnos = [list()]*nPlans\n",
    "    planChoosers = [list()]*nPlans\n",
    "    nDTLs = len(DTLNO)\n",
    "    districtTractList = [list()]*nDTLs\n",
    "    for p in range(nPlans):\n",
    "        if planTLnosList[p] != \"[]\" :  #avoid error\n",
    "            planTLnos[p] = ast.literal_eval(planTLnosList[p])\n",
    "        if planChoosers[p] != \"[]\" :  #avoid error\n",
    "            planChoosers[p] = ast.literal_eval(planChoosersList[p])\n",
    "    for d in range(nDTLs):\n",
    "        if districtTractListString[d] != \"[]\":\n",
    "            districtTractList[d] = ast.literal_eval(districtTractListString[d])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c2140ea-ad78-4a17-b23f-a3758c3ab1aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will first calculate the HDweight for each regional plan and its associated districts\n",
      "Now I will add in the single-district plans from the two large blue counties comprised of whole districts\n"
     ]
    }
   ],
   "source": [
    "nHDs = len(countyNo)\n",
    "nRegions = np.max(planRegionNo) + 1\n",
    "nHDTLs = [0]*nRegions  #for each region, total number of districts drawn across all plans for this region\n",
    "regionChooserPop = [0.]*nRegions\n",
    "planChooserPop = [0.]*nPlans\n",
    "planFracChooserPop = [0.]*nPlans\n",
    "finalHDTLlist = list()\n",
    "finalHDTLcountyNo = list()\n",
    "finalHDTLweight = list()\n",
    "finalRegionNo = list()\n",
    "print(\"I will first calculate the HDweight for each regional plan and its associated districts\")\n",
    "for p in range(nPlans):\n",
    "    r = planRegionNo[p]\n",
    "    for t in planChoosers[p]:\n",
    "        regionChooserPop[r] += tractPop[t]\n",
    "        planChooserPop[p] += tractPop[t]\n",
    "    nHDTLs[r] += len(planTLnos[p])\n",
    "for p in range(nPlans):\n",
    "    planFracChooserPop[p] = planChooserPop[p] / regionChooserPop[planRegionNo[p]]\n",
    "    for d in planTLnos[p]:\n",
    "        currList = districtTractList[d]\n",
    "        finalHDTLlist.append(currList)\n",
    "        finalHDTLcountyNo.append(countyNo[currList[0]])  #any county in region OK for this\n",
    "        finalHDTLweight.append(planFracChooserPop[p]/float(nDistricts))\n",
    "        finalRegionNo.append(planRegionNo[p])\n",
    "\n",
    "print(\"Now I will add in the single-district plans from the two large blue counties comprised of whole districts\")\n",
    "for t in range(nHDs): #KISS, just go through all tracts to find those in Franklin and Hamilton       \n",
    "    hC = countyNo[t]\n",
    "    for r in range(2,4): #Hamilton and Franklin are [2] and [3] in this list.\n",
    "        if hC in regionCountyList[r]:\n",
    "            aDP = countyPop[hC] / round(countyDistricts[hC],0) #county-specific avgDistrictPop\n",
    "            planNo = len(finalHDTLlist)\n",
    "            finalHDTLlist.append(HDTL[t])\n",
    "            finalHDTLcountyNo.append(countyNo[t])\n",
    "            finalHDTLweight.append(tractPop[t] / aDP / float(nDistricts))\n",
    "            finalRegionNo.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e082dff-a0d0-4863-8ec3-6db10b6509f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now we will save the combined single + regional HDTL to a file\n"
     ]
    }
   ],
   "source": [
    "print(\"Now we will save the combined single + regional HDTL to a file\")\n",
    "date = \"27Nov\"  #only change from 15Nov is to assign Lorain area to region 4, not region 0 w/Toledo\n",
    "HDTLno = [i for i in range(len(finalHDTLweight)) ]\n",
    "df = pd.DataFrame( {\"HDTLno\":HDTLno, \"countyNo\":finalHDTLcountyNo, \"regionNo\":finalRegionNo,\n",
    "                   \"HDweight\":finalHDTLweight, \"HDTL\":finalHDTLlist} )\n",
    "outname = STATE+str(nDistricts)+date+\"NosplitALLhdtl.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5aa24e07-7e75-473a-868c-b111265d1e2a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7cffd4c1-c517-4cae-88ea-89b71b6ae7ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Time for some debugging, as eval_postedOhiomaps reports low Dem seat totals\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Xl9cL5eW7rU3LstATCeqXLqXxwosA8O6B2E07ihA1gi6E8KkRCL7NxJtEjeZMP2Usi8HPfY5U4Mb2OcONKsGYkB/bBgubi3uUsCxaSXb+LJJeP6Zp7rbpoa1J5VY77auiZk8gyzJaMIgkO38zvSmS915CiBpBl8Fu8qkR008CwbcO3bRI6U4+rKbppw3pLPbGBO4ljVghN789czg/6VOGV2n9w2XtEYfx90UL8KYS/PfTmZw76dA90sd0bmWS5zu8+mdvka6vA8DQ9qy1bXsIUSPoOjQt6RbTTwLBt45Epnl5c0BzhpaBPg+ji7MUxldixSS+eHM9W8rCeCUXB/QawPEThgHQNxzEPXIMzJ3Jgtmz9pioyWadRdNer3ePtL8vk6lzRI25l9+tEDWCroPI0i0QfGtp8qfRVBm3KpNMZ3ni1Q8YvXE2NM0m1dVDHaSBD1Yu5sl6g5NLCyj0qIzoOZjFc2fiiUepS6Yo9u3+wbFJ1Ph8vt3edldlzfQlxD7cABLYCtgqzhygKoNbRnLJyG4Ff59C+k8a1un7ZBsbUQFLiBqBwEHkfhIIvr1s7SR8/+PPY9Sszp+Xho3BrULmy7kAVIZL+Nhn83GiARKAbXOJJKHYNlW1EYp77/7B0cgFywvsgcztXZXI9A2UpLb3Lm1YXE9D/y0U9irt1H30hgZUwPb7O3X97kKIGkHXQeR+Egi+tYS8znASSelkDYtszRqabK6TTrmAI0cPYvqz7/EB4Ebl6kOOoXxzgpW6TlyyicuQVd149QyBugT03rX+PP6356mqW48iqQR9BZx+zkmYZs7nZx8SNUou+l5tTx13hR8ra2JlTGzdxNYt0C1Kqh0/mKb30xnMpmzvQtQIBDlEmgSB4FtLjwIvXpdCSjf59wdzkHO/Uor3O5gjRw+icVMt05bNAgkOH3YwBx3Ql4O2auOuL94iDUj5XzidwzIt1tctBUC3IZ2I8sjjD+fPB4PBXWr/24SiOz8Sw6Mq6HfY/m3OZ+Npam6bDYCvuPPvxYw4okYO7l3BKESNoOsg4tQIBN9qTMumVIqxbubsfNn5kydi2zZvPvsqumRQ5ipkwulHtrk2VhchjQ42VPTvvkv9MPRtWxzCuQzllmWxdlklqkuhuLwAf9CLZVnfqSSSquU8izvY/hRUsjYGQBYDj7/zU352LlqzHAp1uo3dgRA1gi6D3SRq5D0To0IgEOw5MobFQdIqBmu1+bKy4YdQXBhiyYfzWZHYgGTDSaec3G4cmsrl6wEIST608K458mazzRmPSry9qE1taHXelYujMuX6dzAjzvJuWzIpGSpTu8QGyQbZBNVGVm2GHlrGUac5dqXKdVt475l5SBKobhlVU3B7FAJhL4d8bwS+gCMMIuu+Ithzf2Rl7w6zLst511qofcESXV+HDKRlfZfEXJOoUXOCcW8hRI2g65Cbz5WU786vJIFgX6EhkWawWtuqbMuSL7jn7nlkkhmQYFS3/eg9vH+7129eVwVAsbdgl/vS0lLzs6sv4m/3TCFmbMmXFRQ49zAiGk12YclWqFsCEpLj32cqYIKdgaXTazjqNKfeS3ctQLK3Di5nAnGi9bM44xeT2HDfkfSKzcuf1VGJ217+Kx1L6Iir+Me01cgS/Oyw/px7xKhWLSWTST755BOy2SxFRUVMnDixw2CEVu6HYEdiRE9nceWWnnkL2vq66Oks8lvOe8kqnfenAZCSTgoKV+7d7i2EqBF0GYSlRiD49pLI2szVezDGtSlfZpsGyZQBEgQtLxMKRhD7bBNK0Ino6xlYiJyLNFxTUwNAaWFxp/uwpaqeJx79F7rpiCgAVVXIGs3pFyXTxbN//ogzftUcC8dWdCSzWaj0maAxYER3vvp0DbWLQWo5Uko2Hbn8JCNZElvWtxI0AC4MCqUYR1szOXPqyTSYGhIWj01fnRc1lmWxceNG3n//fdavX5+/NpVKMXny5Hbv9+CDD1JfX4+iKCiKgqqquFwuAoEAZ555JkqLrJPrPlyM4nOjFXjpNW4QiqoQ3dzY/FjDds3PSE7mojXvxRQJIESNoCshLDUCwbeWWFpnodmdGm9fTihpgOrFrc53twrRF9QRWVDXqlzyKPjHVVAbrQegrHtFp/vw1D3vkAw35AWNYnuQZdkJ6JkTIorpJbHBxev/nImUW591zCVDWDRzLXraxO1VOPbssWheN6sWVVJLBjWXVUHPGkhWx8OmN+QitXk1TTaRKm0ApuqlZ2IRAJvtQhpMjTIpxmT3clTT4qabvtzmMzXlq2qP+nrnnZmmiWma+Tg8kUiEjz76iCPHHpavG5qTBbJAnMWr6xjxg0MwjeaAiaEFBhsHr6bnge1b0raHkovWrBV3XpTuDoSoEXQZhKVGIOi6NDbOYUvtVHp0Pxefr23m6KY4NYUBrZWg6a2Vsz6zmWI7gORRsLMWWM2mDjttEp+2Cc2lgALdBvTsdB8tOdPq2JTS3PLH27HkZh8bb8oRTQ0rHEFjSyZDD+jL0AP6tmkvnXCuUz1O3ca62DbvHy7xYdYtBCAp+el2vWOxWXTnJIZnFvCaNRGAbnIUVbI6bKclW0c/Nk2TRx55hHjOhwVg1KhReL1e0uk0CxYscJ7LtinsVcqGoUuxqlNIBviSCl7LjZV7rpJ+FWxkJTISLhQal23utKhRM8679xQVder63YUQNYKug7DUCARdEtu2WLToCjLZzWzY8BQ9epyH21WEy11MQcFYAv5BRNPOQBnyuog1eAhKzi/3unQjSND/lNH0GLcfkPMFsSC9tJ7IG6sxGzMcpQ9ngbWWsv7dOt3PMYfuz6fzN2LLzf4hLQUNgKFsZflQO/YlySQNQMbtyfml+NvmNfJ0S5OucpyN/SEPnlXvAOCzE/k6XiMCwPnHTuSWQ47j1n88B3Ugl/TjopMmkUwmSaVSeL1eZsyYgaIorF+/Htu2mTlzJl988QWyLKMoCtlsFttuPf91/PHH58XPsmXLSKVSeWfokT9qttbMv/s9vA2g+h3TkyzL9LzjUL6+5X0K016MNTG+enI6ikdF8bpQfW6KBldQ1Kesw3cEoOs6rqYUFKXbrrunEaJGAIAZjxN98y1sQ0dyu5Fcrq22rcrcW59vvaEoO53DKW+p2UMZegUCQefIZKrJZDcDYNs6Gzc+3ep8YeEEFq+bDBRR4HWhBIogUQlAQnJ+wZeUFjPzhQ9IJlMEwyHK+lTgDwcpunA/tvxtAQoyA+wKXFrnM2gffdp4jvz+WFLJDKlE2vlMpkmnMrz+0tvoWiOSrRAa/AGZmsFks6UMOKhjHxA9bQIyms8RCOtXVLepM2B0BYu21CAZbgIFXqQVdW3q+E3HwuMJleB2qZhGFgXwhQrp27dvq7r77+/Ekrnlllvy4sWyLCzLykdEbmLw4MH079+/lTWnyXm4vUzkUtY5pwaaz8myTGHaub4k6oUogJHbUkQ+qCPwxzBuX8eJKlP19ci5vnpLxPSToAtQ+9DD1D/xxO5rUJI6Fj1uF7RTbufmZEXwPYGga6HrjqVBVQsYPOj3xBPLMPQI6XQlDY0zaWiYyZjQTP50cA/eWHE83RPZNm08/NQUsrQYlHOuJIot49XceGwXJhbv3LuQiRMnMnHixE71VVZk/EEv/q3isvzvnTeRTci4M3Q/4HnC4TEcNOaFbT932hEB1UtSPHL9W1gGQOsM3741C8F04upkapdS5epNiLnU+gZSsmUFFPQibEdBAn+BY8UwsmncQNDf8dL1SZMmMW/ePHw+H2PGjCGZTOYtOtlslnHjxtGvX9tpwCZRE41GWbJ4Cel1EbSEhCugUZJwvH3cwR3PUp5066ierVd7bVVni7OCypRllL2cV0uIGgEAiU+nA+AbOxY5GMTW9Xa2bKtjsq3Pt8K2sbNZ7GzbL7dtIblcyPtQtE+B4NuAJDcNFTbdup3W6lwqtYllq6awqfIFeoc28eNhzzN71un584NK+rJyy1qykoFsS/T0l2MYBpsz9ZiShSlZxEkTz01XEYcPPvig06KmIzRMdKCsR5aSkmPo1fOi7V/jV8huAUnXMBrarzN7Sa/8svC3vviSo+SNDAFKkivhoYOwAW+ugqY5QsvWHetVONhxSoFJkyYxadKkHXu4FjSJmsWLF7N4sePbdLi+H4PN5oCG3tLWAfKyGLhRaRgEkkvGTBvYGRMsmx4njthu/Jp0LkO3oWk7baHf3QhRI8CMJ8isWAlAj//7K2onluTZtg2G0VYIZbMdCKSm8y3rZdEGD0ERokYg6FLoWWdEd7nafjd4vT04YPjN9Oh5Gc9/fAf7h95g8JBPScSLqOi2AtuWOaCXRlZX8Go9OXjCBZSUDMS2bYx0lsiWRqK1Dcyc9QUrqtcAUF5evtufwSv70c0EvcpHMmrkFTt0zbnXHMmCz5aTiKbJJHUyKZ1MQwpW17LFdByOfUoE250hpUYJqxEqrXISePCSQcbOC54GO4AkO0d+w0kpoMk75iy8MwwYMIAVK1a08rtplJI0ailMN0gVHoYPb3bGNrKOoAHo+/2RBEt3PnheJidqTM+OW4D2FELUCDDrnIBZks/XKUEDOOq8yZ9GIBB8p4gnnDxKqdRaGiNzUZUgLlcYt7ss/8u8tKCcy0/9K6/OGEZ5+d1QvqadliqZO+9kwqGfMm7c1bi8GiW9yynpXc60rz7P1zr22GNZsnEJL3/0MtEtUWzDiQ1jazbhkjBu2000EsWwDYLFQQb2Hsj4wePpU9anw2fQDcdq7A/u+PSI2+1i3JHDMHWTVFwnUNg8aD975XM0ZMqZfHYRPTqyqOhp6hd/xIXPr2ajXcIl/3uNKY8+yrJlywAYOPhlPPfcxQknnNDm0p///OdMmTKFv/zlL1x11VX58kwmw7XXXstzzz1HKpXi6KOP5uGHH6ZnT0eonH/++TQ0NHDFFVfw4v9exLItDj1wAv976+V80MGWLH1yJk2l3nDnklFmcxm6Le/uz6y+s+ySqLnzzju54YYbuPLKK3nggQcA5xf7zTffzJQpU2hoaGD8+PE89NBDDBs2rMN2vv76a/74xz8yd+5c1q1b1+aP2MTDDz/Mn//8Z6qqqhg2bBgPPPAAhx12WNsGBTuFkYt10FlBIxAIvtukUs3B4ObOPTu/36PHDxg65OZWdcf09rFsubOvqkMoKvoRejZGNhulrv5FPJ4qEsmHefudd+je/Qd4PEV4PcW43UkUJYsk2Xz44bWorgz6mtF47Ba//lOgN+ro6Ci5/7J1WRYvX8zi9xeTVbNo5RqXnn0pZeHWq3AMK+sEAQzt/MD91A0zSMW2mmLHsSZNe2E1B9Y0MOSsU9te6PIwv9HL13ZfZCz69O3LrbfdzvtT3wPAkhROOeUU5s+f32qMfOWVV/jiiy/o3r1tDqyrrrqK119/neeff57i4mJ+/etfc9JJJzF37tx85OHzzz+fjRs3cs2PL6cqXcvHUz/kwgsv5PXXX2/TnneNAahkMVDdnZMEek7U2HvZnwZ2QdTMnj2bKVOmMHLkyFbl99xzD/fffz9PPvkkgwcP5rbbbuPYY49l2bJlHWZGTSaT9O/fn7POOourr7663Tr/+c9/uOqqq3j44Yc55JBDeOSRRzjhhBNYvHgxvXvvYo76fRyzwTEtK3s5voBAIOialJUez4YNzkICr6c3qbQjcmLRr1rVa2j4nGXLbwLA7S7nkImvIcvNw4xuXMbUqYegaQ14PKupr78lf668wtlaEpWy+MvOpSBQgKqobK7dTGV1JYZiUFhQiEf2ULO5hlR9Ck/ag6ZrjFrXhw13fUH80N70PaHZH8S0dZAgsJWoMWNZqm7/AoAljZ/zVcMnXPXvV1DU5n63FTTN1Jt9mDO9hiFntX/+wVkRQMVC5uSTT2TTlkbmz5sLwA03/p7nnnmazz//PC9qNm3axK9+9SveffddTjzxxFZtRSIRHnvsMf71r39xzDHHAPDMM8/Qq1cv3n//fY477jiWLFnCO++8w+eff878979A1b388eob+dE1l7Bs2TKGDBmSb8+2bVy2I4SMowtJRuJoAW+HaRk6wog4juT4O2fp2Z10StTE43EuuOACHn30UW677bZ8uW3bPPDAA9x4442cfrrjKPbUU09RXl7Os88+y89//vN22xs7dixjx44F4He/+127de6//35+8pOf8NOf/hSABx54gHfffZe///3v3HnnnZ15DEEOM2epUYqEpUYgELSloOAgjj5qFQC63sicuWeTTK7C53cCtdm2TSZTw/wFFwM2suxh/LjWggbApWoMH/YgXy++D9uKAEkkKYkkZ1DVDJLUOv7KkO6rGHbgQfQobP3juT1qY7XM/ssbjMgMcAqmR1k8632MMqcPB5q9cZsgTZ/O/OXrsMO9CSkmPttNzMjillSGhsezLDKL5/7wG/qOGo1laaSTvUn415HVGpBtBTUbRjX8mEoKb7IbEgqN2TJGPDWCi/a/iN+M/U2rfklb5VTY0uj402RMiZde/B+JRIIJEyYAjpPvhRdeyG9+85t2Zzfmzp2Lruut0iZ0796d4cOHM2PGDI477jhmzpxJOBxm/PjxzHzXWQBy8NjxhMNhZsyY0UrU6MkMcs7rx/dBjPoP5pOSsgR/MIiKYTtuLDCjzjNJXcAfslOi5pe//CUnnngixxxzTCtRs2bNGqqrq1u9cE3TmDRpEjNmzOhQ1GyPbDbL3Llz2wieyZMnM2PGjA6vy2QyZDLNESajuRcvaI1R71hq1KK9G19AIBB0faLRr0gmHYFTXf0K6XQVkcgCbLvpu1ZmzIH/we0uaff63r0Ppnfv/7Ypd4RRHFmWcLn8PPv+aCqUGLNW/oPTxj683X4V+YsYnOqVPzYwKch4IZegu4S+9PScBJuBzXDEmMcZ/fVqSipX49nsVBpWcAgn976crK0z/6v1LJc2YMmrsYLN40hWa14GpWsNBBuHEvE4jrL/XfYCV468HLfWPGX2l9OHMulR532lk3Fmz53PHXfcgWGYhEJBXn755XxsmrvvvhtVVbniivYdmaurq3G73RRu5SpQXl5OdXV1vk5ZmTP1lrEcPyJf2E9ZWVm+ThOKq7UEMNxRJK2BDUsb0LrbeL1FxGKVzJr9CyBDRfkPGTOm7ThuxZw4PF1h5epOi5rnn3+eefPmMXv27Dbnml7Y1p7r5eXlrFu3rpNdhNraWkzTbLfdrf9ILbnzzju5+eabOzwvcBCWGoFAsKMUFR1C3z6Xsnbd3wFobPyi1fnCwvHY9s6FcgBnsYHH0zwo+goPh+ibWI3vUxvfQEmg1zauhkQ8hmY7QeWK/jgaM26w9p1FmPEs2ODdaqgoMqKkXW70ovK8qKlKrWZ44aF4cdPfLmGxe9vjVlZrIFqwlJTpiJhgJo77znIytouk5CUpecnYHsCZTTj6rzNxx6P84he/IJ1Os2ZzI+ecdz5/+PODyHaS++67h7ff/he1tcvxeHb8+9i27VZLqSVJwrIsMrkpN19BoE0dgC3LK/P7pivOmkN+i+VyElPOm99cr2lRU/Xm54F2jBNxJ3qyGt75lVO7m50SNRs2bODKK6/kvffew7ONpVtbv7j2XmZn2Nl2r7/+eq655pr8cTQapVevbf+PsS9i1Du/MlThUyMQCLaDJCkMGHAtHm8vMpkavJ4eLF7SPOXS0DCTefMvZNLh85HlzkcHPn7kLbw97T0KFJ135l7CDya90+r8pjVrif9zBTYWKSWLJVuUU0RKzuDzBcAHIy46tHWj8x/GfPcGPiieQMw7gBGxD+ltJGgAgsWljDnlHBqqFAqXmhiSyYDC/Vi3eSOGOwY29CsfRn1DLRF9c75JlxwjU/AJ3XSDs2NOPiZN0tHQKbSjxGwvLgx0VDZFsvSUZYpy37Xdu3dn1cI5/OeRvzBmTBX19Y0cfPDJ+bYtC37966u5887reOLJkaxdlyabzfKf/xxHMBRCln0oip/Vq7+kZ0+dzz9/gExmBVVVG1ny1ZtovkYM04VbsdmyZUsbw0CqMYELiEpJNh80H8uVwrJkbEtBUdvzI+pg3E/kRE3Bt0zUzJ07l5qaGsaMGZMvM02TadOm8eCDD+aXqVVXV9OtW3P+jpqaml2KO1BSUoKiKG2sMttrV9M0NK3j0M4CBzM3/aQUClEjEAh2jB7dz8nv+/wDWLnybiRJpqFhJpaVRZJ2biIgoyeYuug2GhtnIJkJLCVEwgoQlhvw622Xh0dfWk0wZyEJmM2rbrb4GxmQMZG1dpxdR1+AMvoCJgOTAY4/vE2Vxc9/Dpi4g14uvPJUACL1MVyaC5+/eVD/aO4irMgpAIwb+xrB4DAMPUskFiEZqyediJJJRMgmItxVE2FOphdbrADxVJhNUZtCJYts65iyi4xtc/wJbsaN74lhuJBlA1m2+d1vqzjm2ADHHx9E0xoYNsxCVWHR1ws44ogAAHV1BmvXbuGSny0jkfwbffpmicWSfDzjFxw01unvUy//m0gkgmzdySdv/x0pEyS44lzc0T6E8ZLxJjADj+e8a45i8uRHME2DVKqRTCbK3Lm3o6gfoygJarYsRXMH0bQgbrcfWVaQU04+LXcXWEG7U//qjj76aBYuXNiq7OKLL2bo0KH89re/pX///lRUVDB16lRGjx4NOP4wn3zyCXfffXenO+l2uxkzZgxTp07ltNOao1lOnTqVU045pdPtChyapp/UYiFqBALBzmHbJsHA/ow58Fli8aXMmnUiLlcBkrTj6U7SepyXPjmMcjmKF0AG7Ib8CJVSe7S5xh1r30rfO1ZO5Z9mEDq2D6Gjd35lrJ12ElzaLUJuhYta+4qsr0ty8X/XAf/Hyf3fRtMu5dBDpqG63ISLSgkXlbaqPxI4A7jhhhv44Qkn0KvXocRiMZ5//nk2rlnJY/+eQjh8Bx6/l+9NXgJANpvklpuHsP9+5zFp0klk0o1kMhG+f8oUpjyykKLCo/H6bB75xyf07VvA8GHjyKRTlJenGTMmxn331nPFleXIss4Df6nl4IN99OxnYlAHWh0ULiLY2I+6fm9Q3/eNvJN2927HAqAoKoFACYFACYriiEaXaxMLF7ZekWVZCmW5FVSugm+ZT00wGGT48OGtyvx+P8XFxfnyq666ijvuuINBgwYxaNAg7rjjDnw+H+eff37+mosuuogePXrkVy1ls9l8OOdsNsumTZtYsGABgUCAgQMHAnDNNddw4YUXctBBBzFhwgSmTJnC+vXr+cUvftH5pxcAYIgl3QKBoBNkMlv4Ytb30HXnh5Hb7Tio6no9H38yEll2I0sasuJBUbyoSgBVDeF2F+NyF5G2XTz29dMMd8foq1mkLKjTDqQktB/p1DrM9CpQC/negY+0uXfdkRLd3+64b9HpG7YraizLIvLqalAkwsf0Qva5sVJOZm60jkXZ6181+6K8sfo4JhQs4r777uPYY49tE+akJZs3b+bCCy+kqqqKcDjMyJEjeeeddyjq56NxPaTM5iXRbrcPWVYIhbrTr2+zRenfz5zFb37zG2677dlWwfdaulZMOLieK664gj/+4TUATjrxVO69/Y/43RKzF1+EpDZQ3/8Nant+gOx2fGgymSKCgRPZb7/mFBdN9Op1IitXzUBRUsiyjtwiErIsmyhJGZDQSuw2137T7PaIwtdddx2pVIrLLrssH3zvvffeaxWjZv369a1ySVRWVuYtOwD33nsv9957L5MmTeLjjz8G4JxzzqGuro5bbrmFqqoqhg8fzltvvUWfPh1HkBRsH9u2MXMhrsX0k0Ag2BkikQVk0vXIuZmebLYmf840E5hmYrttnJQbGnQbnqzVWJZZSpmvng/O+mCb142bdARb9q+msaGeTCrlbIk0hR+ZeHQvlYEY/pUNaCENd8iNoiltfDArb/gsv69XxSn7+SjMpA5oqP6O/YEUubkdG5l5S44mYMd4//33tylqHnvssXbLP5p9EwAFLVZWAaxdu7ZNXY/Hw9/+9jf+9re/dXifoqIinnnmmXbPedaeSibrxByS3SmymRBFxRdzxKRLUZT2I8IPHnw8gwcfnz82jCyZTJxMJkYqHSGScAIyBiraJtj8ppHslgkivuNEo1HC4TCRSIRQKLT9C/YBzHiC5QcdBMCQeXORu0BESIFA0PV5/7EH+PK99wEYdckSJBmGDroP046SyWwCJAwjgmHGMfQYhhnHNOOYZpLaxCZqDRlNtrFsmJdUmZdUaDCdH7sSEl/98Ktt3L1jPnr9TVa+5SW71cjmluD2cCp/PF42udwK0Zdm35tAn3XUrQ+i2UWsdk/DNWgIVslAPD4NzevG49Pw+T2s/+JfhBY/wRxrMG9b4yhUoUxOMmHCIYwfdygFhTuXLuDd6T9E1T8FyMcD2pMsXPg86zdMoSB8DGPHXoXL5fS3Mwt69GgtK8c5kf0HzJqGO1S6nSs6x46O3yL30z6O2eCYjSWvVwgagUCwwyQjsfx+/bICspEgCx75JwCyy0Jxg6qB6lZQPQouzYXLU0zdOpklwZ58MsL57vnvsf/kz1OdoKp+l5+EnkCRdi6ibUsK+vclazurkyTIh77bWuR8YSnsj85PWoia+Lo+NC0tSUc1Fn5WBNS3ucePy+7FK8fZX17HRUxltf5jzOxJeD9yE/9oDrp6I/L6lWyZFyJju8jKLrIujaxLQ9c8GG4vpseLosp4rAyRMZspzmX82fjZL1FcQVRXCMUVRtGKULRCFK0I1dcN1d8DaTtZs7fHiBHnMmLEuQDUpmo58rnxAIwpH8M/jvkHHnXHE1OmtzjRpW0V1MDej3UmRM0+jinyPgkEgk5wxEU/Z8UXToyaDdO6tTpn6TKWDnoCHFlh5LYUINMnEsA9tJGsy+K5WU/nrxtWNIxZm2dh2AZH/OcINFXDq3jxql58Lh9+l5+AK0DQHaRAK6DQU8iRvY6k3N+8CjaSW16c8DZw3V/OwMiYvPaHF6iKlnOSvZY3pL75umXeRZA6KH/s0b4kKVs0pqOsSE/Ml9taGtuUwJLAllmZnsgI33v58yG7gjTN01Wy1Y1U1XpsQ8KNgds0QG+2Em1NsLtJU2jYZZl3INNhVWTTxmMohOwSCgsPpmzEb1ADbXNE7Sj3zbkvvz9381xSRmqnRE2mzonxYwekVm4lewshavZxjLqmwHvCn0YgEOw4oZIyDj7zDD7/34utynuPLeXQMy4llWggHW8gnYiQTsXIJmNkkkmyqQSr62rJuiyw4Z3IrPy1Ya05zkldum6H+nHXrLuY84M5qLmUDLGYI2ostwGAqilIuWCAl4/vxZhUAytqEmiqxLnnnY/0/LskNxWjFdZSdPmPKfH68G2Mw21Ov0LKZi7863nN/Vq5jqVvnk6/Yb+mSI/giU5jUO08hjfYjKodR9GAKEVHXI0892eAjuyRUB57gXQ0QToaw179NYFH/tHqGXyzZaywjTZIR+5eikkWEx0TA0O2MCULUwZLkbAUiaRikaSG6tRrLJ/5KhVWX/qMuh1vxYQdemctaSlgjul9DIU7EfQPIFtbBYAdbN8f55tGiJp9nKbpJxFNWCAQ7CzDDju+jag56ITz6NZv3Dav+8kUZ4XNfnpPlrg35svn1cxjZMlIegV7IUkS8WycuB4nqSdJmSnSRpqMmUE3dVJGCsM2MG2zVdvxeArwgGbky9K6M+D6CoJcfPr3W3fmVxdRsFX/SnoGGNKvjmVrioma5aQjKVa8M52lc+qpiZUB5Vy7JMnq0i94b+BrzA3AxrI6rv19cyqfpRuzgASWycAxw7Eti8Tzf6Vx+n+IAYpm0uv+O/AceTp2vBEsC7mg/dQSTZiZCNmGxSRr59BY8wk1mS9JahablHVsWvwDBq88ml6HTtlmG1vzpwl/4nfjfkckE6HMV7b9C7bCqM8FIgx2PtDi7kSImn0cIz/9JCw1AoFg5wi3SKsdKHdxwmVX0nvoEdu8ZtbiT5irrgTgZ2Mv5aP4F7yx+g0s26IuXUdduo6var/ip8N/ypWHXQk4S68r0xF6+pp/fE3fOJ3LPrgMIG+lAUjE04AHucUMSn3a6aenYMcXiNiZOOD4iDzx2+lYqEAZEiZ9wqsZclABvY77JQ13/4ejF9hsKq4kemIDIV8hm6+92JmuAtwBR1ylP36JDbc0Cw6t1Iv36DMBkEI79v2raGG8FRPwVkygmMvpb1ms/vBU1spfA7A8+wEVkdW4wv13+DkBNEXrlKAB0Bsci5oU6ho+mULU7OPkowkX730HL4FA8O1CkiSKe/ambuN6TvjZzfQe2vFy5ur6TfzptV/zub0YS7Hplynl6FEnc4z0fW6deCtvrX2LF5a9wPwaJ+nQQ6sWcHfjZ7jsFIakYcpBxrim8XDBIbzxoLMy6kLXLdT5N3H3bc8gqSCrEt6Vjn+P4nVEhWk2x1TxFBbs8LMNLl1B1VKbQStfxFQ0FLdJ0F1Ht5MPofiKu7GSMdZOnsBvap32D1wFmw6cyGYfGMnmdtL1bqJTp8LqRa3al90S2DZ0MoVQumomS+b9nHpv87L5gcqhOy1odpUma79cEPhG79sRQtTs45j5vE9i+kkgEOw8hu74qyiubftU/OP9e5khfQ0S9EwV8I8znsgvIZZlmZP6n8RJ/U/iyNcvp7b+Y0ylEEv2k6E5IN38TDHzlzQvefbrYfyNYWhsez9/gdMfM9nsoOvdiajpvc67nLF/PapNecO/XqP4iruJPnILmVqzzfmWgqaJ9NKl+MpbTy3FV2fQl8/DNWRM2wu2Q3Tx43y5/jayXgnJsulhD6bf2P/DXTB4p9vaVczGCAByuGuESRGiZh/HEHmfBALBLpDJrTbSthMS4sPkF+SSC/H8D14jHGj7Q2pxYyV1MSeHYLmvlJuGeKlMx3DLKtetjGAoYV4OrGcczVYBe1g9gTIXum5g6haGbqG6FE4/xREkerQpoJ2F4t/xgVcuaL2iq2jSAOo/WZV/hqYfhC3p+/fbyK5dTWTqDJSKvkSnfgC6Tt1DD9Oo2eQvziF5/W3a2B5bZt/MosansNwSgaybEQc8hq9i4vYv3ENYkTgyXSchshA1+zhNS7qFo7BAINhZbMsinXAyU3uDHQsG0zJpkJrj2vx3xhP8dPI1+WPLsvjhtPuYv/55JDuLLQd5+pCfMDDU7Odx3+pX2USYqa5ejMMRKvHelfz28h9ss4/ZiGNJ0OQkkrJz8W/8g4tILK/HFZLwH3Es9Z+swtJt6m+9FGyrTX3vkWfgBcIXO8fRYc1phcxMa0GjeEwMl8y6Vx8isnENqVgU09CxDB2QCJVVUDFyAmZpX2orp9GjRCNRN43V5hegSBTphYw44j1Uz94VE3bEMU2pBV3DhUGImn0cIzcf2lVUtkAg+PaQTiQcvxDgmeuvwu3x4vJ4cHt9aD4/nkAAbzCEr6CQn4fP5pHICwD8teoJRi8dz5ihhwDwyob5LFj3NBLg9g7mDwff0ErQAOi2BBK4jeYoemOPGbLdPmaijpjSlPROP1/h2WeQuO1R9KhNw39fBsBMSWz+98ft1rczaSSthYey1cKfp8ig5IYbqJ8/nS1Lv2adqfDGVVdj04FPzZIG+GQJ3fvVUjZ5C/VbcuWSRHejL0OOfgtZ1dq/9psk5gTVUYv2TCThnUWImn0YJ+9TzlIjHIUFAsFOkmhsnoKJtzMdszXX9DqU5wctpNIT4Udf/ALPvBEMKt+fxVs+B8AfGMWM055uN4jbq+MO4d/rF+GVFAwZVAuKywZu956ZDUuAPrjtyI4/WI7gD67B9dd/osdszEgjpaeOIbuxEiuVcba0TmZLhsIj9sd70HhwuYltrqZh+VIaV69CsW2anqQSP68+819MWwaaBUCB16Ss2IfX70N1uZAUF6vW1dPQ6PgC6YG27+Ir9WAWf/YXVFcAzRVEcwfwaiF8Wgi/J0TQGyboLcDj9nYq9cFOEdMBcJd0PgDg7kSImn0YO5nEzuRUtogoLBAIdhIj4zgJq5qHviMPyAXXS6Kn0+iZDEY2g6Fn0dOOlaR+wwZGJDWiI1zEfTppfSELNy7Mt1erHcvPn30VlyQRUiSCikyBSyWouQl5NA71+lHdPubZ1U57SzdhdfMjax3HSMlsWgn0QZPb8eDdAfz7d6fxi02kNmXo/e6TZNesIbNiJdk1q9E3bkSq3sycBUtYt+FD0m9Ow2ohIibLErLlWJYaVC+mLeNVDXqV++gz4gD6HHk24b5D8/Uty+LvDz1BqvFlJEBX3MgFB2BY76PKzU7JJTwPJs6WBgtI5LYtNGNaMhnTQ8bykLFC9Oh9HUeMOK7V823+8GniC7+g50W/RwuVEl/xCUuSYfYfdgB+bfsSQYo71ih3kRA1gr2M0eDMS0uahiTyPgkEgp0kHXemdgorunHKtb/vsN5bD97PkukfAtCtzssZH/t44OIf4Yu+gS0p6NpQdM8wTFc3Xt+W72wKCrakuDw3A7X4lRoWv7wZJJBkGVmRkBUJRZVRVAlZkUlXjaffpjcolNZTc/UM5/tO8yC5PUguF5JiI9kmVjqDlUxixaLYiShmNI4ZS5JtzEIuP9Sy4SPa7daq/fuQdeWGU9vGY1j4ZJlNfXtSZFl4ymyGThjGIYedRcF+E5CUtkNvNpvlz3+8A8+aOUhArM8YrrrxWgrCQdZsXsGcZW+RzkQxzQSWFQc7iWQnke0kipRClZK45RRuJYNXdUSkIlv45CQ+kkA96ze9CS1EjWUa1F92JwDrYlEGH38UgTeuYixw7LTnSU3oy0mlBfy2X0W7Fh890Yjs6Fo8ZX238Yf75hCiZh+m2Um4aM+bKAUCwXeOVM5J2BMIbrPe6ONPZdnny7CtGmxTR8Lm6iee4Pib7sWSFFZUVvNsdQPF5mr6aypZyyZqWsRsmygycVkmJqvEXS6ibo3ZAzWGbEwQSskgOdMztmVjWjamDjrNVo2+ldPpt+5tAOrWduYp23EuliQktxvZ70MKBMmqTp0zfngp3cePx1VUvFPfqQ31DTx44+/x1a/DQsJ/+OlcfekP89Nw/coH0a/8yh1uTzcMYskY0XSEWLKemtVnAVBceFCretnGzfl927TgzV/njzdWJ2lIZnhg3WbOqSiin6+t/06mNpfMUrZxhcvbnN8bCFGzD2PUNcWoEU7CAoFg52my1HgC2w689tK9a3AHzgHAMmuRIi9wyaOPoYWdXE8jhg7i9J258cnOxz9/8hIZVwFDBkLF2MGkEzrphEE2ZaBnTPSsiXu540tjlHendKCKrWexdR07q2ObFrYlATKSW0XWXMj+AHIwhBwuQi3thlLWAwJF6HUxvMNHoA0cgNzCsh1btw6u+yUAi958hXUzpjH28mvwle/YIL9+zTr+ddON+NKNZGQ3g865lLNOPXZn3kYbXKpKUaiQolAh0xY60Ybjup9jDzirVT29hahxl3eDakcMbrRLSLqan1GV2xdomVonxYUdkLtEMksQomafJh9NWIgagUDQCZpFzbYtNUXdJOqdvIfISgl9BozPC5pdwcjlQhh5ZE/KxvRst86cd52BWp50ChW3XNHpezUsW8rc/zyLZRqAhNvvZ/QvfoWkyPnIwMtqq6C2CvOBP3PUnfdut81lS1bw4u2/x6MniLpCHH7Z7zh6YsdRmTvDpi0LKAMi5mh8mrfVuWxjdX4//cCbfNWtL40ZH9VKEQ8VPkjjxkJ6V60jPjjAutEH0ufi21tfX1cJgB3oOlKi6/RE8I1j5pdzCydhgUCw86TjOzb9dN6fjmL2mKPZXHYgLj3B2F9N3uV7W5aFKTsOwlrBNhxxElFg1xdDvHrrjdTpmVZllb9bxekPP8b3TjmXyq/mU7luDTW2TmNtzXbbmzdvIe/cezOamabBU8IZN9zK6CG9dqmP7ZFOV4IHXFpr0adHa6m++re0tK+4qrKUkqWUxtYex2trSb63lujhpxEa0DyFpdfnnrOLJLMEIWr2afQa5x+kUrLtzLACgUDQHnlLjX/7eX8CiUoCa5xf9pt/9xGbf3c9AN3+fA8FJ5+80/fWG+N5fxpPUceiSko5wstVXLDT92hJS0HTS/OzIR1nTd1m1r77NvtdcCH7XXAhc+69i5rZnxLP+Rp1xLRP5/DZQ3egWVkafBX86JY7GNircwklt4vpfM8HvD3yRZZpsHLcYa0EjXTqfhQcdSJGvBEzEcNMxEi++iF4VZTFzvPUf/wS2BahgU4WdqO+1rk23CI2z15GiJp9GKPKMT26unWNpXgCgeDbxeJpzoqmHYlRY/zgPNRnnmtTXvWb6zolalL1uQjFtoUr3LGo8tWvBcAV8HZY5/B/H0KD4Vh0xtQX4JIUXCiokopbUnFJCqv2q0exJEJJlZ9c/08+/PU1rElFeffRBzne7cbl97N+/hwAsobe4b3eeHc6C5+4H7et0xjqyWV33En30j1nLdfMGpQaKC63SG9Zi+INIasatgukXDe7/e/vFAw/ou3Fv4D0lrWsOewEAFJ/fpmN971E/0/ewVPaN2/tl0JdI5klCFGzT6NXOZPcru7dtlNTIBAIOqZm7art1jE3bdqtA0424uScUs30Np1ULUkio2kkl8yn8a0sqqri1jR6jxuHy+sInSZBAzC3qLH9hlpkgTj5vyfxr1//naqb/0RckfnfP/7Sqqqdi9+zNc+++B7r//swbtsgVtSXa+6+i8I9KAgMw2T//6tBq3QDD7GGh5pPyk4uKrNMaV/Q5PCU9kX5wViyXy5DWRhFsiSy0VpH1DQ2Ok0VbHv68ZtEiJp9mLyoqajYyz0RCATfZjYtXczTv70Cl+bB7fXi9voIFBQy7tSz8Bc4VoiKc8+l4aNpba5NatvO7t0R6VzOISSZV3/1DOmshCTZqDIoqo2qSCDZLJ88mUhhAegJmDWruYGPPuLEgQNxezwUpd3Ue7KMTBVzUuEkdDND1syiG1l0S3f2LZ2NDRv5tKKORm+Gx5c+wy+uvp73/u9eEtkMlgSyJFHmCTDukp+16e9Tz7/B5pen4MIiWT6Y6+66HZ+vY+vR7uCTvz5Od2fGD1uykezmVUySlcuQPnT7q7QG//5p9FgdK8ceCoCntA8AViSGBCgFXccvU4iafRRb1zGblnTv4NJDgUAgaMkRF/6EL/75D2wJGleuwFBk7BbxWfyFRYw75UwAKiYdSfiTj1l27z0YNZvJVFWRjkSoKwgwphP3TtQ5osZQPGw0upN3ELEB3dksySBSXgCAZFkUJpPUt1h+/ubKlQAoFWFgC1956rj1iB/Sv7x/h/e987/X8WzybV6PfcT3Ck/lR/95FduywDCQ3O07zNbV1lP9yj9RsdB7Ded3d9yCq4O6u4vq1esJP/l3AFacfjHfv+M6zGwaMx3DTEYwUzHMTIrgoHE71F66Zl1+f/nPvk/gmMOxI0kkulZYECFq9lHMXOZaJAmloGCv9kUgEHz70KuqKHruRY5cur7NuU8H9STq0/CFC1qVe8vLOeDP9wFQ+el0nvvb3ahW22zXO8Ly95eC1LdV2cHDkhgZHSNjYugmVcmNfFy4EI/p4eLJP2JEr5GUBkp47a672BKLYdg2BjCkoYJ6rQ5Ttjj7zbM5ofQEbj351nbve9XJf+L1x6cS8xv8bcb9HLz/kUiyDNsQKa+8+DqqbRD1FPOHO29Dde3ZodeyLOZefh399RQbSvtw/J+uAkBxe1DcHgjtfPJJV6gIW7aRLAllQSOJpa9hd3MchF1dJJklCFGzz2LmUiQo4TCS0k7ETIFAIOiA6DvvUPXHP2FFo6AoSIqCnW32I6no3ZdDzjmP/Q49osM2CgcNBsCQZVL1dXiLdi6pridVC76++eO+nkrGXP6DVnUee/c+llcvB+DqudegzlaZeuZUTr3xxjbtfbnhSy59/1JiSoxX6l7hN8nfEPKF2tT7zbOXEPMbAPjV7aeXqY8m2fjZ+wSA4rFH7nFBA/DBXx6n/6ov0WWFXvfchXsbubF2FE9pX3q89E9q332O7N8/dAItxx1PY1dx1/HL7BohAAXfOE15nxSRyFIgEOwgViJB5Q03sumqq7GiUTyjRjLg7bcY+tWXZPyOf0ikrJhjbv8z+x9+lGPBaEGmoYGVTzzG5hmf4Skrw2WaINvUL1u8030Zd8oADplxAz1X3UVNyX2sGzaDmsqVreqYsdZJLA3ZwLRM2mNUr1FcO/JaAGRkfFpbwXL/S3/kE8VJwOlPq1x31B+328+XbvwXRwTGUR4YwcQDD9+hZ9sVog1RCp90HILXn3Ih+004YLe1HR56KOHxxwBgazJSzHmX7uKus4JWWGr2UcyGRkCIGoFAsGOkFi5k07XXoq9bD5JE8c9/Rukvf4nkchx9rbEHwcfTCdfUseKQQ8j07UPBiSfQ/8eXoHg8rH7heaK334GW0akDNk4Yx7CfLwcFlqZ+ROq1M/CX7o+ZTWDpKUwjiWmksYwUppnGMtNYVsbZ7CxpaQthvZEXDonxVT8ZWM+mF6u4/fKXnQ43rmfk9A84sNeBxNwxQqUhju13LOUd5ChKpBPc/eXdjNx8BENio1k9t5aBB5Ujt0gRsKJ+OeT8mj/9ySxUddtOztMffpvj/fsDMIAD4LVNfPBZDWN+OoqCwj3jJDz75al019PUBYo4/pZfb/+CncTMBTPELSE7K7rRSnvv9vt0FiFq9lFMYakRCAQ7gG3bNPzrGTbfcw8YBmq3bvS45258Y8e2qjfqbw+y5KY/Yr3+Fq6sjmvVGoz/e5iv//4o6bJiApuq0QBdkXGZFr6Zs1CrVRp+YmAWwbrAi5B60WlMBty5bRuYl0hU+ZuHMd1uER+mcgGLQv04rsdGFNkgFFaQIm/z3tSPUGQPiuJHj0skq714fP3YnKwlHLGYuO40AKY+vphoZjNDxvXG5wohSRKm7VgmTvIevl1Bs3nuMnqv80BOE63VoFfGZkidzrp7ZhP/xUh69inYztvfeWIffwJA44ixe2Sqy0g4/ph2U1JyyUYr7LGNK75ZhKjZRzEbm0RNwd7tiEAg6LJY2SzVN91M5KWXAAgedxzdbrkZpZ28TYrLzfDb78K6+TY2vPE6W159GWXOfDy6jmuTE+gzsd9gRjz+BOuef470Q3/HvQaK/+piy2915KyKbMpIpoJkKci2gmSryLYLGReSpCLjRpbcyLKGImt83G8FW6LN0XuDaouYL+XDCFT8g9Ly1bmC1g7NhgkLnxuCbcjAbACOoxxa/M5bt+x+IvqnGDZcu9GXF1l+YyE1GxdQ1vOAVm3ato0kSeipDPX/WUZQLqRB3cL+N32fnqrK3BkbUF9fQ7EtsfjdNfT82eid/6Nsg0hdI73mTweg5PhdS4oZXTWH6FfTUYNh1GAxrlARaqgUo97Jn2ArtvPpl1DUthm89xZC1OyjNFlqdjUfikAg+PZj1NU5iwbUFlaPmho2XX4FqS+/BFmm7LrfUPTDHyJJ7WdsbkJWVfqcehp9Tj2NdDTCsin/wNq4kbITTmS/444HYPClvyQy6QhWn3cOni0mwTcVjFPPZsJx7a84ao+HZ17FP5a3TkcQdLcIAlc8gKGFfuoA25Jxuc/BNJNYZhrTStO4/EvKRtZR81UxstuPpbqJmY0oqWkoriFgpwh0XwA4PrFa1saWIKtCUUEVXy4+kwPkVyjtPhzLsnj0lz8mnksbIEsqBxVPRvN76ferSSi597r/6Ao+nbqW4hTYybZRh7NZZ9WWz985x94Zf32Mvnqa6nA5h59xfKfaALCMLBvOuwg5andYx5ZyoibQtRaaCFGzj5J3FO5CQZMEAsE3T+LzL1h/8cXIoRDhE08kdNKJSIrCxsuvwKipQQ6H6XH/fQQOOWSn2/aEwoy69rftngvvPwzfxT/G+sejfB4/jNvdZxD+aDoXFjXgV1QCqougy0VA1Qi5vATdXsIuHwHVi2wn+cfy92ma2+mZ8rDRmyaotV6tZFmO6CmvHceIc2/Ll0ci85mjOvFz0vUefnzHa8iKMxy+/NdLWD1jDqrX4ujjZpPSG9jwrz/x9P0fI9kStmRjawpyWmJF+Y+w73mIletX5gUNgGUbzKubSo8jRhGoaF7VNf2heeyfcvYL9m+92mvxl9Wkn1+O34YNBS4M3aT39wey/6gdC44a2VBJ2cvPAJA57VwUtfNiI1O7Pi9ozO4qpCyklIWUaQ7gJ/UIw9pG7FDXSWYJQtTsswhHYYFAAJBdvw5sGysSoeHZZ2l49tn8OffAAfR66CHcffrskXsPuvxK5rz8Mp8ceDAAEYI8WN9RyP1UbgNv9G0COUHz+kn/4vp/XcpGLzxgvcey/zuJoBog4PLTS1pJIaC6Wk+XbVr/Xn5/+MTzWTVvNt5AiMjmKtbPcyKtS5KNqrgJKuXw5sLmwdyWkNLOtYWbE2x88nFm1m9q09vhY4+h/1mthaA/6ix7X+6XOWrygFbnNr6ykv1z9xja6CwZX/raqh0WNbN+8wd66mnWlvbhqKt/skPXdER6ixNoz/LB8A8X5sst08BINGBlk1S9+DDpz15DCnadqScQomafJe8oLALvCQT7NFbMsWZILhe23jwl4tl/f3o//TRKwL/H7i0rCiUX/5gT3nyfTw9wHI8Pda/DxiJpySQthZStkrZdJNGI4/jM6J6hAAz0eelbfAAuu3np+Nvh5si3vyh1XGQq3Rb7tbhvNDo7v//Z86+B9Xqbvo055aj8vrS0Pr9vqgqK0bwsPDj6QPigtag55fI7GHjoyLYPPKYcZm6md8Jkw+oGevVv/lFZkrYAieUVHnTdZFidzsCEycbKKD27t42X05JFL71NzwUzMCUZ//W/R+tk6okmsjUbAbBDrSWCrKi4c4H7zMZcMsvwnvv30RmEqNlHMZpSJBQJS41AsC9T8+c/A7QSNABFF19Mcs5sFL8fueXm8yF5vdv1rdlRGh98kInxJMWN9dQVFPHTwqEcv/+QduvesuhjHt5SgOHux2PH/IMDKxwh9NcLX+CjaU9TG9tMPBsllo2TMFP4ypcCIPUZ2KqdHj3PZvmq+c0FkgR2s/+ItzhNoLj978aWgiajqpirSji773XUZSrZaKzm0N//jIKKknavnXDyQKbNr2FgGtY/vpBlY8s4+Kh+WIZFSe72oy8YRmGpj5l//JReWfhq6hp6/nBUh+8vE0/QeOftFAMLxx/Hed87tMO6O0q2znHsVqoN9Hg9rkDbNAhNP4zlcNdJZglC1OyT2LaNUevM/6qlXSe8tUAg6DpU/uY3HZ+UZWSfr43YWb1kIaYi4y0twxUuQPb7UQIB1FAQNRTCFS5EKyzEXVSE5vfjKa/AG3cC5BU11lJXUMTyLbUcT/uiJmka+f2Poxbjejj+HIUlPTj99Ovb1P/f1KGARSDQOuJtj15nsmzF9UgyXPzAfRSVO5Yfy7KY+tYwVF+WUIuAcuH7Lydyzd+wihXkOkfUmLKCb8IV+KQydCtL4NAenHTmudt8p7Is0/u8ocSeWExvQ4KZW1g/s4a1vX2MRKJRsulZ6gT9Sw0Ow6JGAmtj22zzoxvupE+sjlpfAcfc84dt1t1RkouaBd+qOy5h6B0vtqljRZx+dbUVtELU7IOYjY2Q+1WmCFEjEOzTVNx6C43//R+eIYNBVrCSSaxEov0tmXQsGpaFFY9jxVuvPsp7fzTE29wH8nkmW7G+FP52skLSepCC6kIekjy88MoTuBUvmurFo/rwuXwEVB9zIw1odjG25EZJ6by2qoFCrZBCrZBiTzHFnhLcruapF01yBEjA0zrgXtWaGUi5GatAQa98uSzLSC5HOHn9zdYWM94IgF3sIR4oIrBuA56h38dTPJSEFKXsp6MoHNxz+y8b6D+khM2XH8CS11ZSsCFOiSUxcr3jK+Rusdio24hSWNRIccrKLxXfmhWfL6DHVCfYYOrSqykt2z2Wd1f3HmT4EgD7pcVYt1nIW0WHthoTyIBa2HWSWYIQNfskxhYnzoASDiPv4UyxAoGga1N41lkUnnXWDtW1LQs7lcLMC50kVrJ5f8bT/yS1ZQuqZdFzwGDsRAI7lcJOpSCdQcpmkXUdWTfAtlENk6eOlllXLgENuLLOlEYk03EfmrxLnmxebITb8HL2l78lkC3EkHRMxdmK5LupUNz4PsqwuuQ5cNtILoilV1DiOpNsxmTVqs9QNBXZ68J2Z1CCToJNM15PvHoNqidAZkvOedinEdzsJAKXZZVEcZIBlx+L6nG+RzPRLaQaqlC9AVyeIKon6CSQ3IryHiHKLz2QbNbg4z9/wf4x556b/AqDc3X6DiiiDggjEalPU1DcOgKxZVmsuuGP9LEtlg86kO//dMf+hjtC30vuZulDbyHlFGjk648oHHF0qzp2zBFiamH7U217CyFq9kGaRI1aJqw0AoFgx5FkGSk33dQeQ3qU8fJdN1NQ0Y3v//XRbbZlmSb/++xJFq55AICfFJxJOtybZNBHwkiSMJIk9SRJI0XKSJI2ksSzEaLpWmSjBk0yMW0Ty7YoSnYjkHWsFKrtQjVcYEAaWAuMUFTUqmZn2yJ6tekPgO6JQy4909LNv4TNzn5grUwIFXlBPTaOg+yvT+1BbfcGtBnP0o0qzucp3G3sUJDQvaxs7MeQwpV41CyS5UYxClDtAlx2ESUHhKiulcD2sHn1oTz0iw8BGLl/IeFEHWEjRdXy7hRMGEDtxs3M+vGlZPsPoXDsaPpUriCtuBn951t3m48TOA7BPV/5F5tOvBCAuvdeILl6EUogjLu4OwUjj4HcSi5XUftpJ/YWuyRq7rzzTm644QauvPJKHnjgAcDx17j55puZMmUKDQ0NjB8/noceeohhw4Zts60XX3yRP/zhD6xatYoBAwZw++23c9ppp+XP33TTTdx8882trikvL6e6unpXHmGfJC9qxNSTQCDoJFMXb+adRdVoLhlNldFUBTdeFoRGUhSpR0+ncXnaWimaMLF4aNmj4IYjMsO56pQ/8eZj81k727HWJHpVc8KZxzJiyGBemfoBy96vx1VmEuqjIrtkFBRS6Qyx+gx2rDkmy34XBCgpD9P4dT0L300hYxBW/0t05CHYAR92xiAbSxKt3IIRDePVSpAMkCwJsjpfxNwsyYAHFY9sckBAIhGayDHqp0gGZFWVfx93CjN7jM/fcxEHMNb+nGEsavOcD3/5Y5bWD2FkySKuPHAKtpzFcNdgUEMawIbCt1TcayTqDjwpf91XixuY8Pm9JNN18BEsyZX3A1i/BD5+BYD6PoMJRKJsWrYaX0GYQDiAy7Pry6wD/Q7EdoGkg/7oNHSm5c/V/fgQpHgumWVJ10lmCbsgambPns2UKVMYObL1srV77rmH+++/nyeffJLBgwdz2223ceyxx7Js2TKCwfa9pGfOnMk555zDrbfeymmnncbLL7/M2Wefzaeffsr48c3/cIYNG8b777+fP1aUrhXJ8NuCEDUCgWBX+eOri6iKpNueKHZis5y/ZA3jR+/X9nyOLxZ/RL07gVuXufH7dwHkBQ2Af0MFHz6whjeKZxOq7UaAYohDZnXLVoK0zKWddsc5YOQYisJhNi7+jIWAR44Sdj2Lf+k05CunIQVaR05uiWEYXPjMHRhS0wonBWrS/M18Aft0sAyJV454iMO7aYzOLCCqJ7g7dSBJyYttFWBJIWKuYcTcxUhmDLfRwNJ6x+l5cd3Qdu8pN4L3S8dfpc+6d1nX5zjnzkYKl96+b1JLuq9ehPHD84gCuVSTZGWVtEsj6/KQdXswNC+G5sHyeLG9PvD5kLw+pIAfxe/H5ffjDgVwBwN4QkG0cAC5MIR84VEYc+aBYaAsbu6LHPAjJx0HIK14x3yJvik6JWri8TgXXHABjz76KLfd1hyl0bZtHnjgAW688UZOP/10AJ566inKy8t59tln+fnPf95uew888ADHHnss11/veK9ff/31fPLJJzzwwAM899xzzZ1VVSoqdiwQkaBjhKgRCAS7SjLrDPynHNCdnoVeUlmLlG7w6qxVJHGzbN3mbYqah5ZOASDrsqiocIL72RUJpGo/aVeCbFGE0ObuhGqdlUuNRZVo5TZE3NjYWJKFrICv0I3kM3C5XBx32EEU5fJSmUoSUNBkZzBW7Y0suXk8b1UOxavoHF1RyYBQhs98bn5V7kzdFFohDNls1c/FmjNMSjIobpsBq58l2TiY4QdfTN/eI/nTRwsA2P+A/+PYkrY5sX739pvOjt/L0UetypdblkFCTzJ/9hfAFeiqQunwLfQc8iGpeBIluYanMsegxk1KUo0M17J4IvV44xEMxYUvGSXj0sjKKh49jUdP47Jy1hPLwJ0xIJPYob9le1i5TwXQFYWWJoTs/zUHL9SK25/K21t0StT88pe/5MQTT+SYY45pJWrWrFlDdXU1kydPzpdpmsakSZOYMWNGh6Jm5syZXH311a3KjjvuuPyUVhMrVqyge/fuaJrG+PHjueOOO+jfv3+H/cxkMmQyzR5n0Wi0w7r7EkLUCASCXcXKxXW56pjB9Ctp9rGZP38RS3U3tdHUNq9flFzWpkzuncKu9kPfGL+9+nze/2IGlWvr2X9YX8aNPKqdViCxuYGGJevoMWlUK78S3coCXjSPRFYbj5GYx1uVjrUkZbpYG9cYElrPq4ESyNl7GuS2Y0SxaRHz9SWYXAvAAdWfQvWnfF35Ba+f+0a+Xj9v2ymfpN68BD3gax0QT5ZVglqIkFoAgMswcb0+k8ixDzJhsuOUO/2Of/G3aBGTpVquvfOH7T5/S9KpNPGGGImGKMlIlHQ0RioSJxuLkY0lMONxzEQCM5HATiaRUimkVBI5nUJNp1CzaVzZNO5MCq+RQbOc/rtMs937mT5QtW0HBvym2WlR8/zzzzNv3jxmz57d5lyTf0t5eWvHofLyctatW9emfsvr2rumpb/M+PHjefrppxk8eDCbN2/mtttuY+LEiXz99dcUFxdv3STg+Pxs7YfzTWFGo9Q+9BCR114Hy0Jyu5E0zdncbuSm49ynrLmRXLkyj4YSCCD7A078h0AA2e/LlTUdO5+S273TDmJC1AgEgl3FshxRI2/19RNUbdChMdXWabYlJweO4PX4xwBU12+koqgnmk8lDRiNErIsM3nCoTCh4zbm/t8bzF6oYCoagz94kWNvPTN/LhNPAV48bhP39e9R//UcuOWm/HmtdABL+l7KVOmBdts+JDuMcZFNnJdYiNe2mf+9f5KuX4encg6j17/NsMav+ejNG6D/LwAY6G/rP9TyHVR0C/D0plq8skw3j4sBXo1uHjfDDxrNWz++hG7PP0sgmcDINl/Tp9QPUdiUldu03R4erweP10NJ985/txumxYAb30YCXr9qIj1cNvFYnGQsRioWJx1toH7Zx/T/+6vUB0p2q4Py7mCnRM2GDRu48soree+99/BswwFs64fsaI39zlxzwgkn5PdHjBjBhAkTGDBgAE899RTXXHNNu21ef/31rc5Fo1F69fpmTGWbfvMbEp9M237FXUVVmyN+thQ9Hg3bMLF1vc2WXe1MSgtRIxAIOotpN4ma1t/d+e9ty9r6klacM+J83vzsYywZLv/v0Zym+wmrg0nzMwJbyplyx1386NdX4NZ87V4fXV/DrEUuLMWxgKzYHObITBZVyy2vjjtWei03VBUPGM7AQT1Ys2oTPbpL/K3XFpa3I2j+OuQeJn75JJ6qt1uVH/DmZViSH8vV7Bj7qw3P8UHRwcwsOICKjxYg4Yg8GQnTtpEAe1QRckxnQZHMguUbW7XZXXNxQkmYkrPPp+DlFwkkEwRKmpdI9+leDKviVEodj7e7m6pEhqa/aJ/CACHNRVFx61g0nyWzwKvEQ10rRg3spKiZO3cuNTU1jBkzJl9mmibTpk3jwQcfZNkyx5xYXV1Nt27NERxramraWGJaUlFR0WYV0/au8fv9jBgxghUrVnRYR9M0NO2bT7Zl2zbJL2YB0O2OO/AeMAo7k8HOZLCyWexMFju71XEmg61nsTIZ7FQqH+zKjOdiQMTjrT+TThRODAMzEsGMRHaqj5Km4R4wYPsVBQKBoB1yhhrkrUw1WcMRMx7NjaVnkBQXUovAbamaddh6mqGlffljnyu4f/nf+b8tm+hmVgOrmOkvYl7iTPT14/jfdS/Sr5/NgImDKDloPBvem0PDqhqim2Os3KRhuQry7dqyQqaqBquoEEVzk0k6Fg+P1+mfy+PhlNseYdb8x7hxwQNUt2P8eHzUPxh7wCHwTtvIwJKURSGLoje0Kj978zvMLDjA6QNg2mDSIopehRerwokx03TLJrlXmdF5bFMt2DbvxJ0IvQWlzUKh34DuMH05DaqPeMYgoO35KCyrG3PThopEqIMcUsn6eoqAbLBrTT3BToqao48+moULF7Yqu/jiixk6dCi//e1v6d+/PxUVFUydOpXRo0cDkM1m+eSTT7j77rs7bHfChAlMnTq1lV/Ne++9x8SJEzu8JpPJsGTJEg477LCdeYRvBCsWw047qwJC3zsBeRtWrc5im2Zz5M+c0DHzwieBnUmDqiK5XFttbiSXC3ffPqhFXU9lCwSCro9t25g5VaNuJWrSOVEzbPVjcNt5WEhsKj2B3r96jsrX76PbnFtoMuacAZxqgZIb7b/qth9FxdOZvHEdH0UvoyHTg4alMG9pCh7/OHcHn7O5wKNHGNQ9ycItzo/oJ+9aDoBsZrGU3gC43M3q5c1pN3PD6v9iyRJBCxIoWC0cg8ce4KzcMrp/D7XyrTbPnSk/G7t4PyTVh5HYwhOZah7vcQpn6Crnj+1DvW7SqBskTItyTSVl2tTqBrplMbkkzPCAkzPLsm2e3FjL3zbUkDBMsokkWi7Ke1ELC3pR7x4Es/OJuf2s27CFYQO7tenT7uTDdXVc8vQcAGR3x1NemYZGAMxwwR7tT2fYKVETDAYZPnx4qzK/309xcXG+/KqrruKOO+5g0KBBDBo0iDvuuAOfz8f555+fv+aiiy6iR48e3HnnnQBceeWVHH744dx9992ccsopvPrqq7z//vt8+umn+WuuvfZaTj75ZHr37k1NTQ233XYb0WiUH/5w+85T3zRm1FHcksezRwQNgKQoKMEgSgfL5AUCgWBPEcsYeVET9rb+NZ8wZZBgP2Mhsgpg07v2LWJ/qKC7koKtPBGUFmPn4E0r8MgGeNfTS/uSlemJLK+fQJWaS+hoWxTZW/B5bQrLvIy5+FC8FcVEr/o3G1KlWIoz9dT0CZDdtJH/PXwrVdIaHvPOwpIkTlKL+cMpL+ALlPHuq//j2sab6Zfuwaav1lC2X09cP3sOs3oDVsMWjJotbLrpL1i6hBJOoVe9gBIuA1llkAt+Wvwc0rmXckjhjn8Xy5LEj3uV8uNejoA59GUnVEnG5SLQIiu67PNRmokRc/vZuK56j4kay7K4+r2lvDptrWOCUyV+dPTAjuvnklkSbrvaa2+z221Z1113HalUissuuywffO+9995rFaNm/fr1rfJITJw4keeff57f//73/OEPf2DAgAH85z//aRWjZuPGjZx33nnU1tZSWlrKwQcfzOeff06fPn129yPsBnKmR6vjnB0CgUDwbaUu7kST9bsVPK7W8cISlgIK+KTWuQ6CSvNqqI2DL6XitBvRYw1s+ve1DIy8C+AIGqDeDJMsOZCibB2neG7h9flXobu87O/9imH/fqZNf076PyfybSaRxUymqP96HYtufxTZNlneM8vScIh3es4GJPaTvFxx4j/QfI6lurJ6I3igwAhiP7uRr9yLKfv+ELqN6otS1oPGz19Dr3IWupi1awEwks50f2+g91pYMuJAst87CLeyYw698WSSt+/8M8XTp1F0190ojU57mq7z+qln4bJMXBdcwLHnnUUZWVYDVVV1O9T2zrIpluaMf82mer2z8itU4eeZCw5kZGnHU0t2zt1BKdw9uaZ2J7ssaj7++ONWx5IkcdNNN3HTTTft8DUAZ555JmeeeWbbyjmef/75Tvbwm8dVVobs82Elk8Q/+pjgUUfu7S4JBALBbqM+4QiWokDb3HEpHMuNT3aEz+bD7sfTYz+Mxs3o0c1Iiovuh/8A2aWheoN4ew2HyLtsNkpwn/UPvD32p6ikB0U4uaaqbz+II4v/TuXnhciqTXrWB3jGHd3mvgCa3w1+N74jRpL9w0dktphUDzmYdf3ez2fSXGKnmPzqWfTW3Lx+9mw2lUQgDmEzgIlFeTYE/6ti4/82oqKQXevE8pUL+xA++TQyK1ZjZ1P4Jx3Gwhceo9vGdez35MMse/oRkl4vaa+XjM9P1uvD8Pkw/QEI+JH8AWcFq8+H99VXGF65AYClzz1P4cDmGZDByxcDsPbZf8N5Z5FWnXcspbe9RH5HsCyLX723hJpYBk2VcSkS0xdUYyYNbAkmTejJ4yeNQJW3Lc7kxkYAXN9FUSNoi+R2Ez7jDBr+9S8qr7+eXn9/GN+BB+7tbgkEAsFuoclSU+xvvRDD0HXSslPmzYkarXwA4aEd+0daUWeRiOEtpXzUsa3OZTJZ0raLij4pGlf5SG7RWHPRryg/6yDkwlLkQBjJH0TfsBbZ6yV8+Z15p2Qr5fjKhEd9SqFbQTNUMnaz1Xx9Jks8U0cdjQBkK9wUnT6SVU/PJhx148qFm7OzjjtBpqKcihtax1rLykkyf70PTddRLZNQIk4oEQe27NiLBIa++yY/bCc6vqLrzJk2k0Wasxpq/IGDdrjNjnhqUSVvfby2TbnsU7n9rJGct9+OTW+pMcdS4ykSomafoezX15D+6itSX37JuvMvoNttt1KwDUuUQCAQfFuoSzSJmtaWmrqGKLYkI2PhVZypJC24nQUJ0UoALF+zg6xlmlz6p3u4SZlCP6keEwnfry9Fv3UKekJl83/ntN+WmaXgmr84uxkbkHgw4aZac4SOjI2Vc+rxSBDQimnMNgJQ4C4g2LOQA26YTDqWYunD03EloM7eQCmQ6hZoc7vjf/pD7J9chB5PEI1EiEWiJKNRktEY6WiMTDSKHotjxmKYiTh2PIEUj2OVl+MbMIDe99/j9Kuutk3bvdauZuqtD2OMOZe+0WqGHnjitt/jDrCstjnVQe8BBWQNi7KwhwdPGk7vkHcbV7bGkwtk6+uCi02EqNlDyB4PvaY8wqoTvodZX09s6vtC1AgEgu8E9TlRU7SVqNlS7/yCL7Xq82Wbnv0FteFh9Dr+Knr0b5vYWE7mrBqB5hQ4z7z0Do+od+WPP+vxCw4/9Td43/ozNV+FUDQLKViCndGxdIN0tSOgkrNmUQDYqQSW7oiXmLfZOmO18FK+bfwVyLJM1HAG6GJvcxBXT9AL8lckpr1JIDfVoha3P4BLkoQ7GKAkGKCkZ49263TE9Ccfp6S+Fm+LkCaNwTDetLMa6tMeTm7FCcn1O9VuR2zJWdi69Q0z7ZJDOt2OL7f8PFTSfuDbvYkQNXsQJRzG1a0bZn09/onbCIspEAgE3yLy00+B1tNPm2qdwS4fxAYYmPmagTVfw9MvsEIdRE99HXVyERv6nsHQ712OmnVW0sjh7ixasooLHp9Db7WGi3JNvz/kFo4570oAAudcTqD7vaBo8IfmzJaRv/+Jyr++gF7n5DoyVnyRP5dsJ1RZAUUs35SmcssTLLecZeCa2rqi+cILBGOx/LFvYMergTpLNrc6tte65mcpyE3txF0e5pUNBuDU845te3EnaLKwhXztx5/ZESzDIJBwLD7hDoTe3kSImj1Ietly0l9/DapK6OST93Z3BAKBYLdQl3MU3nr66bI3nYi5m9VSDs08wEhpNRcNUxm5/CF8UoZBxgqQoKddTc81D8FDD+WvTXi788jbc4koAaAGgCq7KC9oABj6PZh+L5gZiFVD0LHuqBVOlF894vRr8T9vxw1EvWBvnccBaKSeKRumtCpL6slWx65crLHoGWcQHjqEQee2Dci3qwx98nEWfzyd+JdfoVRuQrJt1EQCVzrFMrsYQ1bpHa1m3OTv7Zb7RZKOqCn0t3Xw3uE2GiPIuWjSRcJSs2/R+MILAASPPEIEuhMIBN8ZmqafilusfrIsC7NFIN2Ndhm9C8IcfMHpLJ83Cf2dP5L0VlB2wu+oWTqTwoWPMdBcxefWUD42R/HktGLSiuPXcYC8EgBJ3sqBttsBIClgm/C/n8DFTgZsbdQ4kGz0qMSS/YbitgEkXh8vk9WGYrp6gK2T9Y1DS0wnlIzSgzRpK02GDBoap408LX8bU9dx5YLhDfn5zyjo3XuH300io1O7vppQUYjC4m3HcenRuxc9LjofOL/NuSevexKAY8rk3RYWJJbLRbW1GN0ZGrY4/j8Jjxef55uP2L89hKjZQxgNDTS++CIABXtA4QsEAsHeomn6qaVPzZsLm/1CZl1/JF5FIhhwRMrgA4+AA5tz4fUZeiCc+ksaGus5764Z2EjkFhvRx66nqKgcYlBhbwHbJh+CWFZgv5Nh8SuwoXmKSe13AMH9CogtjkBuhVNB/wRnDChnvvvHzA+XYeUEkq4N58hNnzHlqis6fL5UXXNMGP82cuSlGxqZesNdmGtW44lH8CeihNMxXLZJveLG/uADiipKOry+I2KxJF/YBQCcPKmtH1JnSaYd36PyQOfFSLS2Dg2Id8EUCSBEzR6j4V/PYKfTeIYNw7+NdA8CgUDwbaN5+ql5cFyb82cBKAu3n4SyTTsZlyNogEP8jVw4aT+OP/xE6mtrsP52D7Jks3bFQvoOHtl8UVE/51NpMXxJEj3+NwNj/XKsSC0NU/9KRfw97GQBb57enAz5kdtvpuHr+bhMg6rTT6Fb79bBW+tzuQRTWxznZUNVcXnbXxWUikT5/PTzGVi1pt3zHjNL3YbqTomad96Yga646J5qYNThx+/09R2RTTvL3LuHOi9q4nX1aEBaiJp9BzMSof4ZJ+pl8SU/FRGFBQLBd4ZHPlnF5mhO1LSYfpq5qtm6cekzcygJeCj0JBhUvJmyUBjN5cWnBfBrQTyaj5A32EoI/fsPF+T3i0rKWOEewiB9KZVffdRa1FTOdz79Za36Jckyrr5DAch+8QTEwXa3nv6pq9qEZjrWin//9gp6jTuEQGERA0eNJvXeB2hPPNGqvq5p2LbN2umzWPDFImhsxJWIYXl9aF9Mp3fVGqJuH41nXkTJoH6Ee5RTUF7CllNOcp6jW8dWnm0x9auNQClHBTPI7cSw6SxGxhE1PYM7vnx7a1K5FAnZkBA1+wzRt97CikZxDxxA8Njd47UuEAgEe5tISufOt5cCToqElqKmOpLO77+9aDMAdxxyK77YFuIxiANbB/pfXDcE+CUBd9touXWl4xhUuZSJi/5IetFtVMndSBx9O8PrciuFijtejSQlnTtZnoJW5YecfDoz//sMSjqJO52m35PP4NUNkrKMxzDbtOPOZPnkqBMpr1rD0Hbuk1Lc2Hf+heNOPLz5Pax2IgWbSBSU77wjrZ7JMjMbABdMnjBkp6/viIxhgu4kG+1f0HlRk613luuboa6X9wmEqNkjpBYtAiA0eTLSblTZAoFAsDdJZo38/vM/m4CmNn+/3fz9YVz4+Kz8cbHPpNzvTOOkTQ9Z041bzuJWMsiS41Gc1J3BtcTb2OZe+59xI0v+MZf99K/xkKWftY7qqVdhBd3IAIqzLNms34ykeZD9zYOslHGWRdue1hFvJx13PJOOO55ELMbrv/gxwYzjOKtaLQTNb6+jtjaN99+P4U8nKK9aQ1pxUd19ABQUkvEFUBNxLEWl/2WXcOCksa3uUb+pJh8N590Tz8H0+rC9PvD5kfw+1GAALRhEKwhRNmYkg8aNanX9jHdmEnN5CepJJh47ub0/Q6dYHXGEow302QVLjZGL22MXFOx6p/YAQtTsAcw6R8mq3fZsmniBQCD4Jskazi99v1thRM/Wv9QPG1zKaQf04OUFmygJuHnxkhJWfu2cm3zkfFy5HEaWZZExMiTSMb54/1UAKtpJcB0qriB0w2dUrV9OQ20Npa9fSEmqHisFshdY/g6Z35Tj9qYx0i5SBQfjPud2XANGoWQdUSMF2p/+8QeDjDpwLMxdRNrjJnTXXTRs2EzDh5/iffCflCSc7/C4y8OKCScw+uqfc8J+O5Y8OVRWRCMSCjb91i/eZl0DeGXwQSjduyOHQmR9Qa5OD3D6qEq8tbiG4oCbYr9GccBNgdeFS+3cD+U1jY6okdwymrpjiTfbpaERAFmImn0HJbd8OzFtOt6Ro3D16IHSIp28QCAQfBtZV+fEctFc7Q+sNXFnCiroUYnEa/LlTYIGQJZlvG4vXreXrOlYDAo78iuWJLr1GUK3PkN496ED6D1jGeWjIxQNcXxxNL9zP5dPx5WdTuLfV+P644eoRhwkUEJlHTQM+voNuAB3Ruervz7FwLUL6Wk7oq3RE6TymFM55JqfMbb7zjn69hzSj+x/XqZywWIysRh6NI4Rj2PG49iJOHYiCckEJZVrKInXMWT5HHDi/3H6ibeRywdKteTliucXtHsPWQJFllBlGZcqoakKHlXG61bwu1X8mkrQo1Lgc1ES0KgIacyqdiInq55dmz2Qoo5g7IrJLEGImj2C/+DxRF56idjUqcSmTkX2++n50IP4Dz54b3dNIBAIOs3DHzvxY8wWEYNb0pCLX1PgdRPLGDQaElNqNW55ZiQGUOrW8CguvIoLj6KxvNGFu3h/gt7B2723tMkRHJF1XjylNnaPg1FGHIscKsD9/qVOHUnGtizcVhIUUMNtreV1DVHefGMqY999HwDZthm65ksANpX3wzrhJA6+9CImhNvmetpR+o8aQv9R2/aHsUyTz596kdqlK7GiUYhFuajqCx7pPQkAlyJhWjbtvWrLBsu00U0TJ/SM0bZSB8htXYd2ClfEETWaEDX7DqETTiA5ew7xzz7FqKzCSiSIfzJNiBqBQPCtJpObfoqkdCb/5RNe+eUh+NzOMPL5qjoWVzlpBYoDbgpChUxp1KjUZRxPDoim0kCzQzFu0MrWs87Yvo9HbNQEWLeCZYUH0e///tPqXPb1a3B7U/jM2aRuGI7blbPgFDqiJpPJ8OQTz7Ppq3n469ei2ia6LOHKKYaNx5/J/j88l/1G776YMNtDVhQm/vjsVmWnAtdZNspWUZAN06IhmaU2nqE2nqEurlMXz9KQzBJJ6UTTzhZPGyQyJsmsSVo3SRsmGd1CNy2M3LO63btmqdFijsXH3wUzdIMQNXsEyeWi2623ALDm9DNIL16MsWXHU9ELBAJBVySS1PP7yzfHqY1l6V3sDCO/femr/Ln+ZW7Of/cyrJzvxsjQcMo8MQ4sG4MsyST0OAk9zoKqjzk8YJBOf8hTby5Dknwg+5GVALZtg7mZ7hUncvToswj37eU0nmheBt6EHhiG23Qyd2uuTZBzGanNanzw8rtsWb+W5IzXafICinsKef+in3PUxLGMPOgA9vPtWFydb4KtBQ2AqsiUBj2UBj2dbvesF+Yye141ZeW79qy+nKgJdMEUCSBEzR5H7d4NFi8mMX363u6KQCAQ7BL1udxBTfQudgZI27bz/jYA/1rzJ1S/s6x6cvll3Hf8pW3aqqtdTkn8A7q7LdCqgKq2N1Shsfpr4CwiM76gB2CUd29Tzf+HD8jOeRv3G+fSMrPCy/ffj2I1T82Yisbk393GqBFD97n4YU1Tg4W+zqdIsCwrn8yyoAsms4S8nhXsKcquugpwAvLplZV7tzMCgUDQSSzLprGFpQaaV0Olc/FPmlC86wCQjWLuPOaSNm39/ZXzOeLNMwgojoNHvTKZqPtqauWfUW2dR6X+/XzdlFWGnsmy/5ycD4zUvj+Pa7+J6OnmAbsh62klaACUkZM4YOR++5ygAYjm/nYlwc5HE441RlAs529dVLrzkZK/CYSlZg/j7ts3v5+cN59w97a/MgQCgaCrUxVNtykb/Pu3GdY9xNeV0XyZpDYiyY6YePKEx3CrbYeZWY3LkSQbf+5n9XFDf0yovDneS0MiygfTplOoNeANHcnyMWPyv8BH3vL7dvsn+cMov/2aL++9irXVq6lOB/FldHrVR9kc8rO2rD9nnrT7Ug5820imnL9J2S7kfarPJbNMaRp+X+dj3exJhKVmD5PdsCG/r3TRdf0CgUCwPUo7GAxbChoA22qud+37P6Fm86JW500jy2Yrg0cCJWcw8Re0Xv302mf3Uqg1EMkWcWD9QGSj2eKy/pxz+HjcJN649tY2fZELy/BP/ikr4yXEDY3eJUMZUNPIxJWb+OUf/sio4dtfZfVdJZNLZtl9Fyw1kVwMtniga6ZIAGGp2eO4evQAlwt0HXffHQveJBAIBF0NtyozsmeYrzZGOqxzyIBiPltVR0HsJzQGH6PGruPod87j18XjCWph/FqYt9a+ywYFKmRnGkM2bRStOZBfdf1GymVndVO47Co8gZEt10tRmIpACrxTXwX+0KYPAycdxZVjx6P6/FSuWE/k0/cAaDjhWFZ7QzR074dtGOihQob/+pcMOviAXX433waa8j71CnfewhKvczJ0p4LtREvsIghRs4eR3W48Q4aQXrSI9Fdf4e7Zc293SSAQCDpFPNNxPJQxfQopzllzGjdWcGH3KP8KO7/o76v7ok39n3h6AKtxma39WxZvmINLNqhNdeOsI85DlmXCc+ZSv6GKdDLF2rc+oMe//0FK6zigqepzzpX368G8vsPptX4pbsugMBWlcNWX+XprrlrFoM8/3OHn35NUNqZ4/NM12DjisHuBl/KQh7BXRZadSRXbtvlyQyNvL6rmq40RYhkdr6rg01QCTZtHIexxEfa5KfC5KPS5CXlU7JzfU9/QLiSzrG9KZtk18z6BEDXfCEpuPb9Ru3U6N4FAIPj2cM5BvfIJLQEOHVhCPGOwYEMjp43uwdx1zqB3jjKN6+obOTaR5M1+Y4gaKZJWlpiVZb2VIYjMhF7jWZlYjctqPQyl0g14gBJvFb/77+/5tOEEFFlGVWXcLpn9IxI/A1TL4O0pT4FRQJ9QELdfw+3XcAW84FfwFQfwFhRw4tsvYNs29VW1rJu/mOX/+g+DlnyBP5uiV2MVG75cQq9R+32Db7EZ07KZuriaKdNWM299Y778sU/XtKmrSE7QvfbdpHcMm10TNV09mSUIUbPHMRsbSX7u/ErxjRu7ndoCgUDQdfn5pAH89LD+GJaFW5GRJCfqbWMyS3FA46ihZQwo9XPgirehCvq5evP7c99pt62NM34FgIfWFhdVafb5OK70P3y8dDCVqeYcThW5cP/F8XoiLy1kbd/v4eTtzgAZUr6lxEMr8/Vl00Q1TdymicuycPUKsKD30Uh1dbh0nfQ99zF71GSCHpWg143scpMtD1Lr9bB/UYhJpYX0DfoI+rR2Y8h0hi2xDP+ZvZ7nZm1gUy4nkyJLHUZqBjBbnNJUmYqQh6BHJWNYpHWLrGmSNSx008bIBduz7NYRiSUJAh2kuNgRjEZn6tHqwv6hQtTsYer//W/sbBZt6FC0IbsvjbxAIBDsDRRZQmkRDEaRpfy0U/cCL786ahAkFKiCgoPO6rAdI1uf22stFCaNPJ13ZkcIZu4FYOyBpcStnmQNi3TWpHHAobwv1yFTAb7mH4p+vQ4ThairoVV7lqKQVRRaR9gB/DkxZdvM2KKQyIspnXQfL9jwal2MO+tiqIsbUTckcNkGGgYeycQjW4T9Xnr06kHAo4K6mS32HNyqgk/V8Lnd+F0e/G4Nn8tDQPOgKW7eX7WQGVXvk40Ow0gNIRws5MKDRnDRxD7c9sYSXvuykhOGV/C3c0dTl8xSE0tTE83QkMjiVhUOH1xCwU7Gmnnyyw3c9NxXKH7XTl23NXaj827lAmGp2WdJzJgJOKkT9sXYCAKBYB8knXMm9hS0e9oydWK2szy4VmtgzbqpLNVjlPh7MrL0ACYd9CPmfeaImruPHUfA0zrb9rxjRzLzrgUANBSoXPTrMfQudUTKn2/9KxkTDhl1NBMP249UJEI6GuXjjz9iZaT1Si0AJIkjS7PE9QxLkxpuW2d1NIXRYprGDjliQJdUdFTiABZsjMHXizcD4O09BdW/eodej+QBzbMRrexdLGB6qj/aymM4fvQk3lwo8faiat5bspnvjehGecgDPXaoWQzTomZDFM2tUNy92Zm3Ku5IOpe2i8ksc3mf1C6a9wmEqNnjNKVH8Ow3dC/3RCAQCL4h0jnx4Gn7i37J0g+prGwRkE+SWL3qF7iBKPDy0l6U9v8jABYyfq1t5NqA1mxxuODaA+ld0jyFpZvOAB4qDOMvKcFf4gSJW/nqq+12VTINHrzuvPyxbdss/nQeq75axENemWhRCdcPKWLk4aNoiMRpjCWpra3nk5dfQpfdHPrTK0nqFk9tSBIzoVQeDbYH3dTJWjqGpWPazmZjoKkqpmsTPtWDpmo0pBtYHVnNlK+mAFMYt9/1zPw6zO9fWcS4fkWU7GBcmXTGYOif3s0fHxPyo8gSkgTTok60Z88uTp81JbP0CFGz79IkatSKir3cE4FAIPiGWPWB87lVRN+FX73Dfi+dS+XhbfMGJQgiYVJqb4BVjuhJEkCS2loX5ixwrCNJr0zvota5jAw7CxKEC5stFXo2k993ZdMMHTyITDpNMp7AL3n57L8fIskSyUg9y2a8TzrmxBebDHQbchTfP/UaAPrm2qiv3Mj6Z9fj9nq5cGI/AJ5+Lgkm/PPkm+hf0H/H3hMQy8b4ZOMnvLX6LaZvms5C8x7KCm6iphFufHkh//jBmB2y8s/+fGOr4/ejrXNk2RL0Ktm5vE+2bWMBZs43x53L++TroikSQIiaPY5aXIy+cSNWLLa3uyIQCATfLNFNrQ4j6+eiYnPA3DSfHXUZcdlFJrOegH9/Thv2M2qSW3h/7i/oYToB+9qLDltdn6Th1fUA2AcVo8jNtSzLwsJJBxAuahY1L015OL/vcrk4/LgTUGwXT117BREzRuWsZguHg4IW6E0mvoaqZR/y6n0FDJt0EGV9u5NorOO/t9wIQDblOPmalkks63zHh7SdC0wXdAc5qf9JnNT/JO6fcz9PfP0E8cKHkaNX8u7Xm3l1QSWnjt72/NPszzdw4dtf54/PLi+gX7EfGxvbhql1Eb4YEmCYa9ui5ut4ij+t2MSsSALdttustPpfNJfMUoiafRdXr57oGzeS3bAB35gxe7s7AoFA8M3x8Z3Q9zDoe4hznKgBYEXvM/n+mBvbVO8e6MbZh/yH/8z9LT0Tb1CrHdjqfN2mOC/eOguAjEviolNaRwiONibyfsdFJc3iorzfAJZsmQuSRFJSePTWP6LVbwHbBGTcvlKwQVI8FPccxBEXnU1Fv2787Sd/QE8uYOWsl1g566UOHzOWjdEkAcJa55xoG9ONvLn6TQAUTxXBsulEqg/j7neWcvzwCtLxLJpHxet1MWduJY98sILlkRSqDassM9/ORJ+Hu351MHKLVU4L316I7TEplloP+VHDZHpDjFmRBI9s2LLtDto2oYQj3PpWlHfqGb8JhKjZw7h79iQJ6OvX7+2uCAQCwTfD+f+FZ3Mrnz57IC9qXDlRQ6Csw0s9qocfjv8rX9f+jPGhfq3OFVa0sDSMKqBiK3+T+i2NAEi2guZtXiF0xInfZ/8DDuSpJx4nYVhOwBbbBMnLsCN/xvE/P7ZNP5Z+XoXsPhLVLsDMLsO2omAnQHLhDXixTJNjLvklAI0Z575+lx+XvPMrjJJ6kjtn3UlNqiZfdvz/t3fn8VHU9//AX7N3dpPdkIOQQEAEBAkGOX5iULQiBFAxYK14fEFarcWjhaJVznoiqC1CVSjqV61ii1+KVytSQY0VEZVLgyA3Rsgm5NzdbJK95vP7Y7OTLEmAHLuT4/V8PPaxu7Mzs+8ZwX3x+XzmMxdr8d8vTbA7avDXDw9g9bZj8AGQm94NVo4diJzs/mHLhC+Ad03B0OPVSdhfWY2PS534uMyJrx3usEvFAWBikhXzz09DvE4LjQRoJQkaAJLbjYJAcD9sqenCTBkZwPp/ovKLL5D8u9+pXQ4RUeRdkA3EpQGuAuD4F4DfA+iMMFUFr3jSxZ19jGFGUkaDZRqtBtXd9Igp98HSo2FXyrGjwUnrhBRAbm4usrKyYDQGg4/ZFo+Y+AS4S0qg1w+FIe5qSNokDMoa1mA/h74pwsev7YckSdCZhqPvsKvhqfLDU1WKy38xDOdlhrdUOLzBAbTxxvhGj6XIXYQvCr5AnCEOqZZU9LX1hUUfHNy8v3Q/7tp8Fyo8FZAg4ekrnsb4PuOh1WjxdM0PWJV7BCu2NZyMDwAu0OowtX8yDpa44dJJSqCRvQHU7C9F1e5i1BwoA7KDXXHP+dx47psDYftICXiRJvwIaLSYnmzF9IsaHw/krQy20kgmEzQx7fNmlgBDTcTFjh0LPPoYar79Dp4jR2Ds10/tkoiIIu/KB4F/zwF8buDPg4CMKRhaEuw6+kGyoqfHizitFmatBppmTHfh7W5ETLkPpbtKgOvC5/6qqa6bjSY3NxdFRUUYNmwYdu3ahYMHD0KWg+0cerk3NLo4DPh/Keid0bDV4acfypTX05/IgjXpzD/iDk8w1FgNDcfTVNRU4Ib3b4DTW3c5uVlnxoJRC5DTPwe5P+UqLT3PX/08ruh1hbLekJ7hXVmXxsVgl6saPgAf/ToLA/oFa5c9wRDj2HQc/ooa1Owvg/AE0BitHEDP8mL0LitC77IiWGuqlM+Oa7XwD54PXSN3Vg+UB+eoae83ZmaoiTB99+4w9Dsf3sNH4P7iC4YaIuoahs8AAl7gi5XBAcM7XlE+etmhxQPb9gEIDoGJ1Wpg1WkRq9PCqJFgkCTYdDp002uRqA8+dzfocWl8LBJTzPAfqIStwNPgK8/rPgg/VJTAmFKNEs+P2L9/P/bv36983rNnT4wcfgm2vRS8Zc24Xw6Gs6Qa5YVVCPhkGM06pF+YgGpnMBxlTe131kAD1IWaxlpqviv5Tgk0mcmZOOE6gbKaMjy87WFc0O0CpZXnV0N+FRZoAEB72gzD80f2Qd8YA7SxBmiqZdQcKIP3ZCVcn51oEGK03YwwD+uOmIwkzHrpT/Bo9Rj+s59hUFoqDL1i4fX2hMfjgdfrRVlZGXbs2AFZlpX7TJ0uUFFRu9/2ezk3wFATcVW7d8N77DgAIGbY8DOvTETUWWi0wKjfACPvAI7mQhzchD0HtuGwKQ1HLOdBJwH+2nsZuQIyXAEZ8PjOutvYeBm/r339zl92Y+CIFJw/NBmmWD087gBMNd0xsEcPHPF6YbfbERMTg6FDh2LYsGFISUmBHJDxpeYzCFlg9T2fNth/9h0ZKDwaDCEGU+OT1QlZwFlaA51Bg5g4gxJqGhsk7PYFL62+pMcl+N8J/wtZyLh7y93YVrANH+d/jB+dPwJoPBBVyeEjaO789ADeRCzMaLxlS59qgfF8G2IuSoKhtxWSRoLfH7ys3hjwITtzCGy2hjUWFRVhx44dMJlMTYcapaWm/c4mDDDUtCkhyxA+HzS1fbje/HycuOdeIBBA3KSJiLloiMoVEhFFmVYHDBgHacA4DLsWGAbgFwjOgVIjC1QGAnD6A3D5Zbj8AXiEgFeWUeELoMznR7k/+Hy82oMdjipUxmiwv5ceF57woWBfOQr2lSP3zQPoeUE8Av5gCDDF6jH92ukoLy9HSkpKWHeKRqvB+UOTcGR38GofjUZCYq9YFOcHx4xs+ds+yP5gC0lirzg05pM39uOHLwsBAN1SLai4rgJA46Gm0lcJADDXXk6tkTQYlDAI2wq2Yc13a5T1LkxseFPNytNaX4ohYIeMCxIt0MYaILwBCL8MfQ8L4q87H1pbw4n6iovrrmratGkTXC4XEhMTERMTA4PBAIPBgMrK2hrNTV/yrbTUsPupa6jYsAElq/8K38mTMGVkQPh88BwIDsgyDR6MtCeeULlCIqL2Q5IkxGglxGg1SDac2xVDXlnG3spqfHKeC31rJHQ/VoXDu4pReqISJ36ou+dTTJweZrO5yR/pK28diNQB8YiJ1aN3RiJMFj3kgIz/vPw9ju6uCwEWW+P3WCo6Vjc+ptzuhrM2FDQWaqp8wTErsfrYuv3q62ZAjjPEYfrg6RjVY1SDbS2G8JaiX13WF1dOHtxoTU0JtdQAULriTpw40ei6Foul0eUAUPnZfwF08lCzdOlSLFiwALNnz8aKFSsABNP3o48+ihdffBHl5eUYNWoUXnjhBWRkNBzJXt+GDRuwePFiHDlyBP369cOSJUswderUsHVWrVqFZ555Bna7HRkZGVixYgXGjBnTmkNoE45/fwD7wkXK+5q9e5XXhv790Gv1amjO8IeFiIjOzqDRYLjVguHW2v+fXgiMvKYvKk5V4ejuYhzZdQrOkhr0HtxwxuL6YuIMGDo2PWyZRqtB9q8y8P5f9qDgcAUGX5aGuERTo9uXF9YNrjVZ9PhRBAcW2wxNt9TUDzK/HPJLDEoYhBRzCvrH9w+7QWh9k4emwajTwu3xo2+yBf/vvOZfSp2eno677roLx44dg1arhdfrRVFREeLj4+H1euHxeOD3+yHLMi655JIm96OJC7ZaeX440OQ67UGLQ80333yDF198EZmZmWHLn376aSxfvhyvvfYaLrjgAjzxxBMYP348Dhw4gLi4xpvyvvzyS0ybNg2PP/44pk6dinfeeQc33XQTtm7dilGjgun1rbfewpw5c7Bq1SpcdtllWLNmDSZNmoR9+/ahd+/eLT2MNlH22msAAOu11yLpvntRvXsPNBYLDH3Pg7F/f0hN9FESEVHrxXc3Y/iEPhg+oU+r9qPVa5Az52L4fTIMpuDP4yOPPAIAyB56Jc7LM0H4ZVwco4VXCPgEkHl1L3z3XSmSzN3w9x/+jtFpo+EXflj0Fpi0JhRUFgAIb6nRa/QNBgU3Rq/V4NrM1FYdEwCkpaUhLS2tVfsIlAWDW7ebp7W6nkiShBCnz4R8VpWVlRg+fDhWrVqFJ554AhdffDFWrFgBIQTS0tIwZ84cPPTQQwAAj8eDlJQUPPXUU/jNb37T6P6mTZsGp9OJDz/8UFk2ceJEdOvWDf/4xz8AAKNGjcLw4cOxevVqZZ0LL7wQU6ZMwdKlS8+pbqfTCZvNBofDAau1eVNZn8lP996Hyo8/Ruy4q9Fr5UpI2tbdCZWIiNTn9Xrx5JNPAgDSA4mY4Lv4jOtPHfh71GgaXpUFAL/J/A3uG3ZfW5cYNUcmToL3+HH0/tvfYBnVdItOpJzr73eLmhDuvfdeXHvttRg3blzY8mPHjqGwsBDZ2dnKMqPRiCuvvBLbtm1rcn9ffvll2DYAMGHCBGUbr9eLnTt3NlgnOzv7jPuNluT77gU0GlRu+RhHxmej4t130YKsSERE7YhOp8OQIUPQvXt3DPWfpywXmUlw9bDAdHEyYobUdXXZ/LFh22uk4E+sUWtEZnJ4r0ZHE7r6SdeOZxMGWtD9tG7dOuzatQvffPNNg88KC4OjwVNSwmdbTElJwY8//tjkPgsLCxvdJrS/kpISBAKBM67TGI/HA4+nLjU7nc4m120N04UXoscjD+PUn5fDV1AA+7z5qPnuO6QsWsSuJyKiDkqj0eDGG28EAJyY97myvHrXMUhSAGVlOmiEBiaYIYSMkT0yUWUBZg+fjX7xwTnJ/LIfQgjotc2/fUJ7IWQZgdrfT20jl4S3J836xf3pp58we/ZsrF27FiZT4wOoADS4TboQ4qy3Tj+XbZq736VLl8JmsymP9PT0JtdtrW433YQBn+UiefbvAElC+d//gbJXX4vY9xERUfSY6g0+NuviEKuNh9kbC5MveIWVq2gXnrj8Ufxl7F+UQAMAOo2uQwcaAJBdLqC290HTzkNNs1pqdu7ciVOnTmFEvbtNBwIB/Pe//8Xzzz+PA7WXMBcWFiI1tW5w06lTpxq0stTXo0ePBi0u9bdJSkqCVqs94zqNmT9/PubOnau8dzqdEQ02GpMJSXffDUgSilesRGVuLhLv+FXEvo+IiKIjcfqFkN0+vPSbX0GvMcCgMUGnMUAvGSBJGvTftQnauN+qXWZEBBzByQUlsxkaQ+OXubcXzWqpufrqq5GXl4c9e/Yoj5EjR+K2227Dnj17cP7556NHjx7YvHmzso3X68Vnn32G0aNHN7nfrKyssG0A4KOPPlK2MRgMGDFiRIN1Nm/efMb9Go1GWK3WsEc0xAwdCgCoOXgQNfv2ReU7iYgociRJgjbWgMmLF6BnViasF/VEYfVRFJV/h/jvP0bKjTdAauSeSZ2BMvFeO2+lAZrZUhMXF4chQ8JnxbVYLEhMTFSWz5kzB08++SQGDBiAAQMG4Mknn4TZbMatt96qbDNjxgz07NlTuWpp9uzZuOKKK/DUU08hJycH7733HrZs2YKtW7cq28ydOxfTp0/HyJEjkZWVhRdffBH5+fmYNWtWiw8+UmKGD4ehfz94Dx/B8VtvQ5+/vaYEHSIi6rh6DhqMnoPqJsATXi+EEMpM8p1RqKWmvU+8B0RgRuEHH3wQ1dXVuOeee5TJ9z766KOwOWry8/PD7i8xevRorFu3DosWLcLixYvRr18/vPXWW8ocNUDwsu/S0lI89thjsNvtGDJkCDZu3Ig+fVo3L0EkaIxGnPf3v+PknDlwb/sSJ377O/R9ewN0SUlql0ZERG1IMhiauBNT5xGoqA01HaClpkXz1HRUkZqnpimBSjeO3zwN3sNHED9tGlIffSTi30lERNSWyta+iaInnkDchAnotXKFKjVEdJ4aOjfaWAt6LArePqHirbdQ+cUXKldERETUPAFHBYCO0VLDUBNhlksvRbdbbwEA2OcvgOfwYZUrIiIiOnfKmBqGGgKA7vffD0O/fvCfOoXjt9wK9/av1C6JiIjonMjKQGGGGgKgsVjQZ+0biBk+HLLLhfxf/xrOjRvVLouIiM6ixlOIwsL3cfDQE6hw7FS7HFUoA4U7wNVPDDVRouvWDb1ffQVxEycCPh9OPvAHnFq5EkKW1S6NiIgaUVj0L3zxxWX4ft/v8dNPr+LAD4vVLkkV7H6iRmmMRvRc/mfYbrgBkGWUrv4ryt94Q+2yiIioEYcOLQl7X+k+oFIl6upIk+8x1ESZpNGgx8N/VJrxvCdOqlsQERE1KiVlctj7i4e+qlIl6gq11LT3+z4BEZh8j85MBAIoeOAPCFRUQGOxoNtNv1C7JCIiasQFAxaif7+HIIQXGk3MWW/M3BkJWa7X/RSvbjHngKEmijyHDsG+aDGqv/0Wkl6PXi+8AOOAAWqXRURETdBodOjKP5VyZSVQO/azI1z91HX/S0WZNz8fx26aBlFdDcloRNpTT8Fy6aizb0hERKQSf0kJAEATF9ch7m/FUBMlVTt2QlRXAwD6fbgR+rQ0lSsiIiI6M39xMNR0lHsXcqBwlEgGg/K6Zv9+FSshIiI6N/7iYgAMNXSauOzxiLn4YgCA66OP1C2GiIjoHPhLakNNcrLKlZwbhpoo0RgMSLr3XgBA1a7dKldDRER0dkpLTTJbaqgeuboahU88DgAQNTUqV0NERHR2daGmY7TUcKBwlFTn5cH3Yz4AIPHuWSpXQ0REFM69/StU5uZCMhggGQ3QGI2o2fs9AEDbQcbUMNREiaTVAgheFhd/440qV0NERBSu4MEH4T91qtHP9CkpUa6mZRhqoiQmMxPa5CQEiktQvXsPLKMuUbskIiIiAIDw+5Wupvhp04LLPB4Irwe67ikw/7//p2Z554yhJkokvR767ikIFJfAc+AHhhoiImo3Ak4nIAQAoMfiRZB0HTMecKBwlLi3bUPN98G+SeHzq1wNERFRndCduDVWa4cNNABDTdS4PvlUeV366qs4cs21qNrNS7uJiEh9gfJyAIC2W7y6hbQSQ02UaGJilNeBkhJ4jx6FfcFCCK9XxaqIiIjqWmq08fGq1tFaDDVREjfuaqD2CqgQ77FjKHtjrUoVERERBYVaanTx3VSupHUYaqIkZuhQ9Fn7BhJmzkTaM88gtXYivrLXXoMIBFSujoiIurLO0lLTcUcDdUDmYcNgHjYMACC8XtgX/xH+4mL4S0o6zBwARETU+fiVMTVsqaEW8BUUBC+f02igMZvVLoeIiLqwztJSw1CjEs/hwwAA06BB0MbFqVwNERF1ZYHyCgBsqaEWkt1uAMHbJhAREamJLTXUKpJeDwCo/u47uHJzIWpnciQiIoo2zlNDrRI7dixMmZkQ1dU4Metu5P/yV6iuvRsqERFRNLGlhlpFYzKhz99eQ8IvfwlJr0fV9u04fuONKFi0iBPyERFR1AhZRsDhAADoOKaGWkoTE4OUhx7E+R9+COv1kwFJguOfG1Awf4HapRERURchO52ALAMAtDabytW0DkNNO2Do1RM9n34aPf64GADg2rxZ5YqIiKirCM1Ro4mNhWQwqFxN6zDUtBNCluH+cjsAIKZ2gj4iIqJI6yzjaQCGmnaj5IVVcH30EaDTofsD96tdDhERdRGdZY4agKGmXQg4HCh96SUAQPK99yDmootUroiIiLoK5XJuttRQW/CdPKlc8ZQwc6a6xRARUZeidD918DlqAIaadkFJx3o9oOF/EiIiip5ARRdtqVm9ejUyMzNhtVphtVqRlZWFDz/8UPm8qKgIM2fORFpaGsxmMyZOnIhDhw6dcZ8+nw+PPfYY+vXrB5PJhKFDh2LTpk1h6zzyyCOQJCns0aNHj+aU3q7pUlOhTUwEfD64v/hC7XKIiKgLCbXUdPQ5aoBmhppevXph2bJl2LFjB3bs2IGxY8ciJycH33//PYQQmDJlCo4ePYr33nsPu3fvRp8+fTBu3Di4a+9z1JhFixZhzZo1eO6557Bv3z7MmjULU6dOxe7du8PWy8jIgN1uVx55eXktO+J2SJIkWCdOBADYFy1GzcGDKldERERdhb8TjamRRCtvOpSQkIBnnnkGY8aMwcCBA7F3715kZGQAAAKBALp3746nnnoKd955Z6Pbp6WlYeHChbj33nuVZVOmTEFsbCzWrl0LINhS8+6772LPnj2tKRVOpxM2mw0OhwNWq7VV+2prcnU1jt96Gzz79wMAYkaOQMqDDyImM1PlyoiIqDM7/j//g+odO9FzxbPKP7Dbm3P9/W7xAI5AIIB169bB7XYjKysLHo8HAGAymZR1tFotDAYDtm7d2uR+PB5P2DYAEBMT02CbQ4cOIS0tDX379sXNN9+Mo0ePnrVGj8cDp9MZ9mivNDExSP/rasSOHQtoNKjesRPHb5qGkr+uUbs0IiLqxAIlpQAAbXwX634CgLy8PMTGxsJoNGLWrFl45513MHjwYAwaNAh9+vTB/PnzUV5eDq/Xi2XLlqGwsBB2u73J/U2YMAHLly/HoUOHIMsyNm/ejPfeey9sm1GjRuH111/Hf/7zH7z00ksoLCzE6NGjUVpaesZaly5dCpvNpjzS09Obe7hRpU9JQfqqF9D/009gveYaAEDxCy/AX1amcmVERNRZ+QoLAQD6tFSVK2m9Znc/eb1e5Ofno6KiAhs2bMDLL7+Mzz77DIMHD8bOnTtxxx134Ntvv4VWq8W4ceOgqb2aZ+PGjY3ur7i4GL/+9a/xr3/9C5IkoV+/fhg3bhxeffVVVFVVNbqN2+1Gv3798OCDD2Lu3LlN1urxeJQWJCDYfJWent4uu58aczRnCjwHDiDhjl+h++zZHX76aiIial+E14sfMocCAC74anu7vfdTxLqfDAYD+vfvj5EjR2Lp0qUYOnQoVq5cCQAYMWIE9uzZg4qKCtjtdmzatAmlpaXo27dvk/tLTk7Gu+++C7fbjR9//BE//PADYmNjz7iNxWLBRRdddNYrq4xGo3KlVujRkcRdfTUAoOx/X8HhCRNRuZVXRhERUdsJ1LuQR2OxqFhJ22j1pChCiLDWEACw2WxITk7GoUOHsGPHDuTk5Jx1PyaTCT179oTf78eGDRvOuI3H48H+/fuRmtrxm8rOJOmeu9H9D3+ANjkJfrsdP911F8reWItWju0mIiICAMjuYI+IZDJB0ulUrqb1mhVqFixYgM8//xzHjx9HXl4eFi5ciNzcXNx2220AgPXr1yM3N1e5rHv8+PGYMmUKsrOzlX3MmDED8+fPV95/9dVXePvtt3H06FF8/vnnmDhxImRZxoMPPqis88ADD+Czzz7DsWPH8NVXX+HGG2+E0+nE7bff3trjb9cknQ6Jd/wK/bdsgW3qVECWUbRkCZwfNN6VR0RE1BxypQtA52ilAYBmxbKioiJMnz4ddrsdNpsNmZmZ2LRpE8aPHw8AsNvtmDt3LoqKipCamooZM2Zg8eLFYfvIz89XxtkAQE1NDRYtWoSjR48iNjYW11xzDd544w3E17te/sSJE7jllltQUlKC5ORkXHrppdi+fTv69OnTikPvODRGI1KfXAJNbCzK33gDjrc3wHbdtWqXRUREHZy/9oIbXUKCypW0jVbPU9ORtOd5as6F59AhHJ18PaDX48K879Quh4iIOjjH+++j4MGHYM66FH1efVXtcpp0rr/fHb8DrQvRhpK0z4dTf/oTEmbOhC4pSd2iiIiozQlZRvFzz8FfWASNxVL3iLVAq7yOVZYjEIA2KQna+HhIknTO3+M/dQoAoO/ePVKHElUMNR2ILjERtilT4Hj3XZS+/L9wvPc+zvu/t6Dv5AOmiYi6muo9e1C6+q8t2lYbHw9NXBy0cXHQxMVBExcLbZw1+BwbB4219rPYOFR/G2z11zHUkBpSlz6JuOxsFDz4IPzFxXB+8AESm7gFBRERdUz+4hIAgD4tDdbJkyG73XWPysrgc5UbgcraZfVmzA9UVCBQUQFfM75Pl5zcxkegDoaaDkaSJFhGXQLh9QIADOefr3JFRETU1gKOCgCAceBAdP/9nLOuL4SA/1QxhNcDUVODgKsScqULAaer7tnlQqDSBdlZ++yqhOxyQjLFKPOidXQMNR2RJEEyGCC8XlR++ilir7qqWX2oRETUvgUqHADO/c7ZkiRBn9I5upBao9WT71H0acxmpD3zNKDRoGL9P1H1zTdql0RERG0o1FLTXm9b0F4x1HRQcVddhfif/xwAULH+nypXQ0REbSngCLXUMNQ0B0NNBxZ/0y8AAK5Nm+A9cULlaoiIqK0EKioAnHv3EwUx1HRgpiFDYM66FMLnQ/Ff/qJ2OURE1Ebk0Jgadj81C0NNByZJEuJ/fiMAwP3fz1WuhoiI2ooypoYtNc3CUNOBeU+cQPFzwRYaXY8eKldDRERtJcCWmhbhJd0dkOz1ouS551D66muA3w9dairSV69SuywiImoDQgiOqWkhhpoOpmb/fhQ+8iiqv/0WAGAZPRo9/riYt0ogIuokRHU1hC84HzBbapqHoaaD8JeVoXjFSlSsXw8IEZyr5umnEDdunNqlERFRGwq10kCvh2Q2q1pLR8NQ084Jnw/lf/87ip9/AbLLBQCwXjMJ3e+/H/qePVWujoiI2lr9OWo4W3zzMNS0Y5Wff46ipcvgPXoUAGC88EL0WLgA5pEjVa6MiIgiRQk17HpqNoaadshz7BhOLXsKlZ99BgDQJiQgec5sxP/855C0WpWrIyKiSOIg4ZZjqGlHAi4XSlatRtnatYDPB+h0SPif/0HSPXdDa7WqXR4REUVB3eXc8eoW0gEx1LQDIhCA4513cOrZFQiUlgIALFdegZSH5sF4fl+VqyMiomiqa6lh91NzMdSorGrnThQteRI1+/YBAAznnYeU+fMQe+WVKldGRERqqBtTE69uIR0QQ41KfHY7Tj3zJzg3bgQAaGJjkXTfvUi49VZIBoPK1RERkVo4pqblGGqiLFBRgZKXXkL5G2shvF5AkhB/441InjMbusREtcsjIiKV8eqnlmOoiZJAZSXK176J0ldegex0AgDMI0ciZcF8mAYPVrk6IiJqL5SWGoaaZmOoiYKKDW+j6OmnIdemb+MFF6D7/XNhueIKTqxERERh6k++R83DUBMFJatWQXY4YOjbF0n33A3rNddwvhkiImqU0lLTrZu6hXRADDVRoImLAwCkLFiA2DGXq1wNERG1V+F36GaoaS6N2gV0BbqEBACAv7RE5UqIiKg9k10uIBAAAGi7xatbTAfEUBMF2qTgVU2B0jKVKyEiovYsUF4OANCYzdBweo9mY6iJAl1CMNT4a2cLJiIiakyo60muqoK/uBhyVRWEEOoW1YFwTE0UaBOD3U8BhhoiIjqDQO2UHwBwaMwVwReSFGy5sVjCHw2WmaExB5+18fGIvfJKaIxGlY5EHQw1UaBLTAIA+MvY/URERE0zZWTAlJEB7/HjkKuqACEAISC73ZDd7mbtq9uM6eixYEGEKm2fGGqiQJfIgcJERHR2uoQE9N3wTwCAkGWI6mrIVVWQ3W4EaoNN6H3wUQW5qva5dpnz3/8O7szvV/FI1MFQEwXa2pYaDhQmIqJzJWk0kGq7lpCcfO4byjKcGzdC3ys9csW1UxwoHAVKS01ZGQd8ERFRRHlPnAAA6Hv1VLmS6GOoiQJt6EaVPp9y3yciIqJI8NWGGkM6W2ooAjQGgzKrsJ9dUEREFCGBSjcCtRel6Hv1Urma6GOoiZLQrMKBMl7WTUREkeE7GWyl0dps0Nb+Y7oraVaoWb16NTIzM2G1WmG1WpGVlYUPP/xQ+byoqAgzZ85EWloazGYzJk6ciEOHDp1xnz6fD4899hj69esHk8mEoUOHYtOmTQ3WW7VqFfr27QuTyYQRI0bg888/b07pqtPGxwOom1iJiIiorfl++gkAoO+CXU9AM0NNr169sGzZMuzYsQM7duzA2LFjkZOTg++//x5CCEyZMgVHjx7Fe++9h927d6NPnz4YN24c3Ge4tn7RokVYs2YNnnvuOezbtw+zZs3C1KlTsXv3bmWdt956C3PmzMHChQuxe/dujBkzBpMmTUJ+fn7LjzzKQndb9ddOgU1ERNTWfAV2AIA+LU3lSlQiWqlbt27i5ZdfFgcOHBAAxN69e5XP/H6/SEhIEC+99FKT26empornn38+bFlOTo647bbblPeXXHKJmDVrVtg6gwYNEvPmzWtWrQ6HQwAQDoejWdu1hZMPzRP7Bg4SxS++GPXvJiKiruHU88+LfQMHiYJFi9UupU2d6+93i8fUBAIBrFu3Dm63G1lZWfB4PAAAk8mkrKPVamEwGLB169Ym9+PxeMK2AYCYmBhlG6/Xi507dyI7OztsnezsbGzbtu2MNXo8HjidzrCHWtj9REREkSa7KgFAuTilq2l2qMnLy0NsbCyMRiNmzZqFd955B4MHD8agQYPQp08fzJ8/H+Xl5fB6vVi2bBkKCwtht9ub3N+ECROwfPlyHDp0CLIsY/PmzXjvvfeUbUpKShAIBJCSkhK2XUpKCgoLC89Y69KlS2Gz2ZRHuop9jKHup0B5hWo1EBFR5ya7g6FGGxerciXqaHaoGThwIPbs2YPt27fj7rvvxu233459+/ZBr9djw4YNOHjwIBISEmA2m5Gbm4tJkyZBq9U2ub+VK1diwIABGDRoEAwGA+677z788pe/bLCNJElh74UQDZadbv78+XA4HMrjp9oBVGpgSw0REUVaINRSE8uWmnNiMBjQv39/jBw5EkuXLsXQoUOxcuVKAMCIESOwZ88eVFRUwG63Y9OmTSgtLUXfvn2b3F9ycjLeffdduN1u/Pjjj/jhhx8QGxurbJOUlAStVtugVebUqVMNWm9OZzQalSu1Qg+1aLvFAwACHChMREQRIrtcAAANW2paRgihjKcJsdlsSE5OxqFDh7Bjxw7k5OScdT8mkwk9e/aE3+/Hhg0blG0MBgNGjBiBzZs3h62/efNmjB49urXlRw1baoiIKNICDgcAQKviP+LV1KwbWi5YsACTJk1Ceno6XC4X1q1bh9zcXGVemfXr1yM5ORm9e/dGXl4eZs+ejSlTpoQN8p0xYwZ69uyJpUuXAgC++uornDx5EhdffDFOnjyJRx55BLIs48EHH1S2mTt3LqZPn46RI0ciKysLL774IvLz8zFr1qy2OAdRwVBDRESR5qsdj6rv0UPlStTRrFBTVFSE6dOnw263w2azITMzE5s2bcL48eMBAHa7HXPnzkVRURFSU1MxY8YMLF68OGwf+fn50GjqGohqamqwaNEiHD16FLGxsbjmmmvwxhtvIL42BADAtGnTUFpaisceewx2ux1DhgzBxo0b0adPn1YcenTpQgOFHQ4IWYak4WTORETUduSaGgRKg7PW61JTVa5GHZIQXee20U6nEzabDQ6HI+rja4TXix8yhwIALtj+pdJyQ0RE1BY8x47h6KRrIJnNGLhzx1kvpulIzvX3m80FUSIZDNBYLAA4qzAREbU9f6jrKTW1UwWa5mCoiSJlrhqOqyEiojbmKygA0IVvkQCGmqjiYGEiIooU5b5PXXQ8DcBQE1WcVZiIiCKFLTUMNVHFlhoiIooU5XLuNLbUUBQooYYDhYmIqI0pLTXsfqJoUG6VwJYaIiJqQ0KW4au9nRC7nygq2P1ERESR4C8pAXw+QKOBrnt3tctRDUNNFCmzCrP7iYiI2lBojhpdSgokvV7latTDUBNFSkuNo0LVOoiIqHPxl5UBAHQJCSpXoi6GmigKhRo/L+kmIqI2JLtcAACtrWvenTuEoSaK6s8o3IVuuUVERBEWcAZDjSaOoYaiRLmJpd8P2e1WtRYiIuo8ZJcTAKC1xqlciboYaqJIYzJBMpkAcLAwERG1HbbUBDHURBlvaklERG0t9A9l7/Hj6haiMoaaKOOswkRE1NYkoxEA4PspX+VK1MVQE2U6zipMRERtzHLpKACAxmpTuRJ1MdREGWcVJiKituYvKQUA6JKSVK5EXQw1UVY3Vw27n4iIqG34S0oAMNQw1ESZNp4DhYmIqG35S0OhJlHlStTFUBNl7H4iIqK2FigOhhotW2oomrShgcK8VQIREbURdj8FMdREGbufiIiorflLOVAYYKiJOs5TQ0REbUnIMkNNLYaaKOOMwkRE1JYCDgfg8wEAtIkcKExRFGqpER4P5OpqdYshIqIOL1DbSqOx2aAxGFSuRl0MNVGmsZgh6fUA2AVFREStx0HCdRhqokySJGjig9NYBxwOlashIqKOzl/MUBPCUKMCrTUUapwqV0JERB2dMvFeFx9PAzDUqEJrtQJgSw0REbVeINT9lMyWGoYaFWhttS01ToYaIiJqHT9nE1Yw1KhAawu21MhsqSEiolZS5qhJZKhhqFGBxsYxNURE1Db87H5SMNSooG6gMFtqiIiodXhJdx2GGhXUjalhSw0REbWcCAQQKCsDwNmEAYYaVShjajhQmIiIWiFQXg7IMiBJ0CUkqF2O6hhqVKC01FQw1BARUcuFup60CQmQdDqVq1Ffs0LN6tWrkZmZCavVCqvViqysLHz44YfK50VFRZg5cybS0tJgNpsxceJEHDp06Kz7XbFiBQYOHIiYmBikp6fj97//PWpqapTPH3nkEUiSFPbo0aNHc0pvVzSheWrY/URERK3gLy4GwIn3QpoV63r16oVly5ahf//+AIC//e1vyMnJwe7duzF48GBMmTIFer0e7733HqxWK5YvX45x48Zh3759sFgsje7zzTffxLx58/DKK69g9OjROHjwIGbOnAkAePbZZ5X1MjIysGXLFuW9Vqtt7rG2G1pbPAAOFCYiotbxnzoFANClpKhcSfvQrFAzefLksPdLlizB6tWrsX37duj1emzfvh179+5FRkYGAGDVqlXo3r07/vGPf+DOO+9sdJ9ffvklLrvsMtx6660AgPPOOw+33HILvv766/BCdboO3TpTnzKmxuWCCAQgdeCARkRE6lFCTfdklStpH1o8piYQCGDdunVwu93IysqCx+MBAJhMJmUdrVYLg8GArVu3Nrmfyy+/HDt37lRCzNGjR7Fx40Zce+21YesdOnQIaWlp6Nu3L26++WYcPXr0rDV6PB44nc6wR3sQuk0CwC4oIiJqOZ8SarqrXEn70OxQk5eXh9jYWBiNRsyaNQvvvPMOBg8ejEGDBqFPnz6YP38+ysvL4fV6sWzZMhQWFsJutze5v5tvvhmPP/44Lr/8cuj1evTr1w9XXXUV5s2bp6wzatQovP766/jPf/6Dl156CYWFhRg9ejRKa2dRbMrSpUths9mUR3p6enMPNyIkvR4asxkAIDPUEBFRC/lPBcfU6BlqALQg1AwcOBB79uzB9u3bcffdd+P222/Hvn37oNfrsWHDBhw8eBAJCQkwm83Izc3FpEmTzjj+JTc3F0uWLMGqVauwa9cuvP322/j3v/+Nxx9/XFln0qRJ+PnPf46LLroI48aNwwcffAAgOKbnTObPnw+Hw6E8fvrpp+YebsTUzSrMcTVERNQyfrbUhGn29V8Gg0EZKDxy5Eh88803WLlyJdasWYMRI0Zgz549cDgc8Hq9SE5OxqhRozBy5Mgm97d48WJMnz5dGXNz0UUXwe1246677sLChQuh0TTMXRaLBRdddNFZr6wyGo0wGo3NPcSo0Nps8NvtvFUCERG1mK+2J0SX0jnGnLZWq+epEUIo42lCbDYbkpOTcejQIezYsQM5OTlNbl9VVdUguGi1WgghIIRodBuPx4P9+/cjNTW1teWrJjSuhi01RETUEgGHA4HaeWoMfXqrXE370KyWmgULFmDSpElIT0+Hy+XCunXrkJubi02bNgEA1q9fj+TkZPTu3Rt5eXmYPXs2pkyZguzsbGUfM2bMQM+ePbF06VIAwSuqli9fjmHDhmHUqFE4fPgwFi9ejOuvv17ptnrggQcwefJk9O7dG6dOncITTzwBp9OJ22+/va3OQ9TV3SqBoYaIiJrPc/gwAECXlgptXJzK1bQPzQo1RUVFmD59Oux2O2w2GzIzM7Fp0yaMHz8eAGC32zF37lwUFRUhNTUVM2bMwOLFi8P2kZ+fH9Yys2jRIkiShEWLFuHkyZNITk7G5MmTsWTJEmWdEydO4JZbbkFJSQmSk5Nx6aWXYvv27ejTp09rjl1VmtBl3WypISKiFvAVBLueDOlspQmRRFN9PJ2Q0+mEzWaDw+GAtd5l1WooeuYZlP3vK0iYORMp8x5StRYiIup4yta+iaInnkDchAnotXKF2uVE1Ln+fvPeTyrRWnn1ExERtVzAUQEA0MbHq1pHe8JQo5K6MTW8+omIiJov9I/i0O8JMdSoJnSrhFDSJiIiag6ZoaYBhhqVhP4QypynhoiIWiBQURtq4hlqQhhqVKLhmBoiImoFdj81xFCjklCy5pgaIiJqCYaahhhqVBKaUVjU1EA+bUZmIiKiswlUVACoa/knhhrVaGJjgdpJCNkFRUREzSFkua6lplu8usW0Iww1KpE0GmVaa5ldUERE1AwBhwOQZQCArls3latpPxhqVKSxcbAwERE1X6CsDACgsVoh6fUqV9N+MNSoSJmAj5d1ExFRM4RCDVtpwjHUqCg0WJgtNURE1Bz+snIAgDYhQeVK2heGGhUpE/A5GWqIiOjcBcqDLTUMNeEYalQUmqsmlLiJiIjOhT/U/ZTA7qf6GGpUpO/ZCwDgzf9R5UqIiKgjCc1Ro41nqKmPoUZFhr59AQDeY8fVLYSIiDoWfwAAIOl1KhfSvjDUqMjQ9zwAgDc/H0IIdYshIqIOh78d4RjxVKRPTQUAiKoqyC6XcjUUERF1XuVv/R9c/9kEjc0GbXw8dN26QRvfDdpu8cFnmxVaqzX4eVwcJF3Dn2rZUwMA0BhN0S6/XWOoUZHGZILGZoPscMBXWMhQQ0TUBZxavhxyM6by0Fgs0Nis0NriobUGA0/Nvn3Bz+JiI1Vmh8RQozJ9zzR4HA54jx+H6YIL1C6HiIgiSPZ6lUCTfP9ciOpqBCoq4C8vR6C8AoGKCgScDsgVDshVVcFt3G7Ibjf8BfYG+9MlJUe1/vaOoUZlxvPOg2fffvgLC9UuhYiIIixQWhp8odcj8c47IUlSk+sKnw8BlwsBhwOy04mA04mAwxkMPU4nNDExiLvqZ1Gpu6NgqFFZ6J4dwudXuRIiIoo0f2lofpmEMwYaIPj7oEtIgI4T7J0zXv2kttoBYMLPUENE1NkFSksAANpEBpVIYKhRmaQLtdT4VK6EiIgiTWmpSUxSuZLOiaFGZZLSUsNQQ0TU2flrW2rYpRQZDDUqU+YfYPcTEVGnFygJDhTWJiWqXEnnxFCjMslgAADIHq/KlRARUaTV3YiSoSYSGGpUFrptfKD2DzoREXVeoYHCOrbURAQv6VZZ6Lbxzg8+gGX0aGjMMdCYzdCYzZBqn5VHTEyj02UTEVHHEBoorOVA4YjgL6TKQt1PAGBfuPDs6xuNSsDRWOoHH0twWf0QZDFDCltmqX0OX0+KiTnrfAlERNR6/trJ93S8pDsiGGpUZhlzBRLu+BV8BQWQq6og3FWQq6shV1WFPRAI3mZeeDwIeDwIlJe3XRGSdFr4MdcLThZo4mKhjbPWe46DxmKGrls36FJToe/ePSycERFRQ0KWlaEG2kR2P0UCQ43KtLEWpPzhD2dcRwgB4fUGQ89pYUeurobsrr/MDREKRe7T1jttW1F7XxEIAVFVhUBVFQItOQhJgi45GfrU1GDISU2FvkcKJLMZkl4PjcEAyWCApNdDMprqWopiYoItTTExkIxGthYRUacWqKgAZBkAoOvWTd1iOimGmg5AkqRgt5PRCLThXwQhyxA1NaeFnWolHAWDkRuyqxIBl7Pu2emCXFUFf1kp/PZCCK8X/lOn4D91Cvj225YVo9HUhpwYaGJqW4qMRkCvg6TTQ9Lpgg+9DtC1YJk+uPyMy2rfN7pMrw8GL6Mx+JoBjIiaKXTfJ218vHKLHGpbDDVdmKTRKGNyWkoIgUBZGXwFdvjsBfAXFgZfFxVC1HggvF4Iny/47PVC9nggV1dB1IYn4a29lF2WIbvdgNvdstaiaKoNmaGgKRmN0JiMkAxneG0yQWM0hL82moLrxMRAY44JtmKZQp8Hn4P7CD5LGl6sSNSRhcbTsOspchhqqFUkSYIuMRG6xETEXDSk2dsLvx9ybWuRCHWTVVdDrqqG8NRA+PwQfn9wxmV/7evTlynvm7ks7H1Ty/yAzxd+GwshIGpqgq1cbXguz0YyGGqDjgEaowmSyVj7XBeCNCZjMCwpn4VCUd1nwWdjw2X1Q5TJxBYpojZWN0iYoSZSGGpIVZJOB21sLLSxsWqXckZCiGCLk8cD4fFArvFAeD3BYOPxQHi8EJ6zvK7d5vTXwUBXXbu8JuwZ9cJUqLULQHRasyQpPDCFWo9CgSnUElU/ROn18J08CSHLdSE0EIDw+yDp9MGB57VX5mnMluCzsqz+c/3PLMGWLLZUUQendD/xyqeIYaghOgeSJAWv8DIYgLi4qH2vCATCg06NJxiSampqw1VNXXCqqYGo8UD2BJ+Dy+o919QonynrnBaiRE0NIETtlwuI6moEqqujdrxnIilhKBh2tGYLpNB7U0zYVAZKKKoflk57LZnNbImiqPKXhFpqOEdNpDQr1KxevRqrV6/G8ePHAQAZGRn44x//iEmTJgEAioqK8NBDD+Gjjz5CRUUFrrjiCjz33HMYMGDAGfe7YsUKrF69Gvn5+UhKSsKNN96IpUuXwmQyKeusWrUKzzzzDOx2OzIyMrBixQqMGTOmmYdL1LFIWi0kiwUaiyUq3yeEAHy+hq1Gp4Wp8GWeBqFKYzDCeMGA4KBrrQ6STgvh8wcHnocGoIeeG1tW7zl0tUirrtBrjCQFB6dbzOEBqdEQZGnks9OmQOCcT3QW/rLaUMPZhCOmWaGmV69eWLZsGfr37w8A+Nvf/oacnBzs3r0bgwcPxpQpU6DX6/Hee+/BarVi+fLlGDduHPbt2wdLE/9TfvPNNzFv3jy88sorGD16NA4ePIiZM2cCAJ599lkAwFtvvYU5c+Zg1apVuOyyy7BmzRpMmjQJ+/btQ+/evVtx+ERUnyRJgMEAbZRbpJoiascvnTEAuauC47Kqg+OyAm53cHyWu956oUcoKAkBCBF8XVWFAErapmBJCg85YcHH0iAEhXfHnRaQlG3Y9dZZKDez5B26I0YSItTW3DIJCQl45plnMGbMGAwcOBB79+5FRkYGACAQCKB79+546qmncOeddza6/X333Yf9+/fj448/Vpbdf//9+Prrr/H5558DAEaNGoXhw4dj9erVyjoXXnghpkyZgqVLl55zrU6nEzabDQ6HA1artSWHS0QdnBKUGgs87vDwc3oYkt1uyNXhy0TtNhEjScHwExsLTawF2ti42tenv7dAGxsLTe17bWztNnFx0FhiGY7agWM3TUPNd9+h16oXEDd2rNrldCjn+vvd4jE1gUAA69evh9vtRlZWFjweDwCEdRlptVoYDAZs3bq1yVBz+eWXY+3atfj6669xySWX4OjRo9i4cSNuv/12AIDX68XOnTsxb968sO2ys7Oxbdu2lpZPRF2UFJpBOyamzfbZ6JxPShA6bcLMKneDdUSDdarqut6EgFxZCbmysnVFhsJRXFww8Fhqg1FccKB+6L02rnb56e9DyxiOWixQUnszS7bUREyzQ01eXh6ysrJQU1OD2NhYvPPOOxg8eDB8Ph/69OmD+fPnY82aNbBYLFi+fDkKCwtht9ub3N/NN9+M4uJiXH755RBCwO/34+6771ZCTElJCQKBAFJSUsK2S0lJQWFh4Rlr9Xg8StgCgkmPiKittcWcT6er36IUcLkgV7ohu4PhJuAKPjd4X1mJgLsScv33lZXB26zUC0f+1hQmSUrIUcJRXFxdS5GlXlBq7H3t1Y5dbaC2EAL+0C0SkjhQOFKaHWoGDhyIPXv2oKKiAhs2bMDtt9+Ozz77DIMHD8aGDRtwxx13ICEhAVqtFuPGjVMGETclNzcXS5YswapVqzBq1CgcPnwYs2fPRmpqKhYvXqysd/offiHEWf9CLF26FI8++mhzD5GISHX1W5R0rfgRVMJRbcCRK92QK131Xlc2eB+odNX7LLRdvXDkckF2uVoXjjQapVutfuA5U5da3fvgMm2spcOEI9ldFby6EGypiaRWj6kZN24c+vXrhzVr1ijLHA4HvF4vkpOTMWrUKIwcORIvvPBCo9uPGTMGl156KZ555hll2dq1a3HXXXehsrISfr8fZrMZ69evx9SpU5V1Zs+ejT179uCzzz5rsrbGWmrS09M5poaIqJnCwpGrXitRZb2WIXf4+4C7XnByuRBwu+vCUVsJhaO4WGgt9VqD4uq60DSxFmhDY4vqvw+ta7VG/P5z3vx8HMmeAMlsxqBdOyP2PZ1VxMfUhAghwoIDANhsNgDAoUOHsGPHDjz++ONNbl9VVQXNaf2zWq02+BdICBgMBowYMQKbN28OCzWbN29GTk7OGWszGo0wGo3NPSQiIjpNWMtRcnKL9xMKRw271Oq1DrnP0qXmdkN2uYJjjmS5bVqO9PpgK1BcXDDwxMUFg1GctXZcUVwwOMXFBUOU5bSWo9qpFyStttHd181Rw8u5I6lZoWbBggWYNGkS0tPT4XK5sG7dOuTm5mLTpk0AgPXr1yM5ORm9e/dGXl4eZs+ejSlTpiA7O1vZx4wZM9CzZ0/lqqXJkydj+fLlGDZsmNL9tHjxYlx//fXQ1v7hmDt3LqZPn46RI0ciKysLL774IvLz8zFr1qy2Og9ERBQFYQO1u7d8PyI0OWS9LrVmd7HVBiXUzs8UKC9HoLwcvrN/fdPHZzZDWxtw6nepya7gQG92PUVWs0JNUVERpk+fDrvdDpvNhszMTGzatAnjx48HANjtdsydOxdFRUVITU3FjBkzwsbFAEB+fn5Yy8yiRYsgSRIWLVqEkydPIjk5GZMnT8aSJUuUdaZNm4bS0lI89thjsNvtGDJkCDZu3Ig+ffq05tiJiKiDkiSpbnB2a8KRLAevNnO5gq1FynNtEHJVQnY565bVdqGFutKUwdi1tzQRVVXwV1UBxcWNfp++Z8+WF0tn1eoxNR0J56khIqJIkL1eJfCED7Cu7WJzuyF8PlivmwxDLwab5oramBoiIqKuTmMwQGMwAN26qV1Kl8YZlIiIiKhTYKghIiKiToGhhoiIiDoFhhoiIiLqFBhqiIiIqFNgqCEiIqJOgaGGiIiIOgWGGiIiIuoUGGqIiIioU2CoISIiok6BoYaIiIg6BYYaIiIi6hQYaoiIiKhT6FJ36RZCAAjewpyIiIg6htDvduh3vCldKtS4XC4AQHp6usqVEBERUXO5XC7YbLYmP5fE2WJPJyLLMgoKChAXFwdJktQup0NwOp1IT0/HTz/9BKvVqnY5nRrPdXTwPEcHz3P0dIVzLYSAy+VCWloaNJqmR850qZYajUaDXr16qV1Gh2S1WjvtX5b2huc6Onieo4PnOXo6+7k+UwtNCAcKExERUafAUENERESdAkMNnZHRaMTDDz8Mo9GodimdHs91dPA8RwfPc/TwXNfpUgOFiYiIqPNiSw0RERF1Cgw1RERE1Ckw1BAREVGnwFBDREREnQJDTRd38OBB5OTkICkpCVarFZdddhk+/fRT5fPS0lJMnDgRaWlpMBqNSE9Px3333XfG+2eVlZXht7/9LQYOHAiz2YzevXvjd7/7HRwORzQOqd2KxLkGgBdffBE/+9nPYLVaIUkSKioqInwk7VukzrPH48Fvf/tbJCUlwWKx4Prrr8eJEycifTjt1tnO87fffotbbrkF6enpiImJwYUXXoiVK1eedb9HjhzB1KlTkZycDKvViptuuglFRUWRPJR2L1LnurCwENOnT0ePHj1gsVgwfPhw/POf/4zkoUQcQ00Xd+2118Lv9+OTTz7Bzp07cfHFF+O6665DYWEhgOAszDk5OXj//fdx8OBBvPbaa9iyZQtmzZrV5D4LCgpQUFCAP/3pT8jLy8Nrr72GTZs24Y477ojWYbVLkTjXAFBVVYWJEydiwYIF0TiMdi9S53nOnDl45513sG7dOmzduhWVlZW47rrrEAgEonFY7c7ZzvPOnTuRnJyMtWvX4vvvv8fChQsxf/58PP/8803u0+12Izs7G5Ik4ZNPPsEXX3wBr9eLyZMnQ5blaB1auxOJcw0A06dPx4EDB/D+++8jLy8PN9xwA6ZNm4bdu3dH47AiQ1CXVVxcLACI//73v8oyp9MpAIgtW7Y0ud3KlStFr169mvVd//d//ycMBoPw+Xwtrrcji8a5/vTTTwUAUV5e3tpyO6xIneeKigqh1+vFunXrlGUnT54UGo1GbNq0qW2K70Baep7vuececdVVVzX5+X/+8x+h0WiEw+FQlpWVlQkAYvPmzW1TfAcTqXMthBAWi0W8/vrrYcsSEhLEyy+/3LqiVcSWmi4sMTERF154IV5//XW43W74/X6sWbMGKSkpGDFiRKPbFBQU4O2338aVV17ZrO9yOBywWq3Q6brU7cYU0TzXXVmkzvPOnTvh8/mQnZ2tLEtLS8OQIUOwbdu2Nj+O9q4l5xkI/n8gISGhyc89Hg8kSQqbRM5kMkGj0WDr1q1tegwdRaTONQBcfvnleOutt1BWVgZZlrFu3Tp4PB787Gc/a+OjiCK1UxWp68SJE2LEiBFCkiSh1WpFWlqa2L17d4P1br75ZhETEyMAiMmTJ4vq6upz/o6SkhLRu3dvsXDhwjasvOOJ9LlmS01QJM7zm2++KQwGQ4Pl48ePF3fddVdblt9hnOt5Dtm2bZvQ6/Xio48+anKdU6dOCavVKmbPni3cbreorKwU9957rwDQZc+zEJE510IEWyAnTJggAAidTiesVutZt2nv2FLTCT3yyCOQJOmMjx07dkAIgXvuuQfdu3fH559/jq+//ho5OTm47rrrYLfbw/b57LPPYteuXXj33Xdx5MgRzJ0795xqcTqduPbaazF48GA8/PDDkThcVbWnc92ZtdfzLISAJEltdZiqi8R5BoDvv/8eOTk5+OMf/4jx48c3+f3JyclYv349/vWvfyE2NhY2mw0OhwPDhw+HVquN5KFHndrnGgAWLVqE8vJybNmyBTt27MDcuXPxi1/8Anl5eZE67IjjbRI6oZKSEpSUlJxxnfPOOw9ffPEFsrOzUV5eHna7+gEDBuCOO+7AvHnzGt1269atGDNmDAoKCpCamtrkd7hcLkyYMAFmsxn//ve/YTKZWnZA7Vh7OdcAkJubi6uuugrl5eWIj49v9rG0Z2qf508++QRXX301ysrK0K1bN2X50KFDMWXKFDz66KMtPLL2JRLned++fbjqqqtw5513YsmSJc2qRafTIT4+Hj169MD999+PP/zhD80/qHZK7XN95MgR9O/fH3v37kVGRoayfNy4cejfvz/++te/tvDI1NU1Bzh0cklJSUhKSjrrelVVVQCCV4PUp9FoznilQSgHezyeJtdxOp2YMGECjEYj3n///U4ZaID2ca67ArXP84gRI6DX67F582bcdNNNAAC73Y69e/fi6aefPqdj6Aja+jx///33GDt2LG6//fZmBZpQLUAwUJ46dQrXX399s7Zv79Q+103tV6vVduwrzVTq9qJ2oLi4WCQmJoobbrhB7NmzRxw4cEA88MADQq/Xiz179gghhPjggw/EK6+8IvLy8sSxY8fEBx98IDIyMsRll12m7OfEiRNi4MCB4quvvhJCBEfmjxo1Slx00UXi8OHDwm63Kw+/36/KsaotUudaCCHsdrvYvXu3eOmll5SrJHbv3i1KS0ujfpxqi+R5njVrlujVq5fYsmWL2LVrlxg7dqwYOnRol/wzfS7nee/evSI5OVncdtttYf8POHXqlLKfxs7zK6+8Ir788ktx+PBh8cYbb4iEhAQxd+7cqB9jexGpc+31ekX//v3FmDFjxFdffSUOHz4s/vSnPwlJksQHH3ygyrG2BYaaLu6bb74R2dnZIiEhQcTFxYlLL71UbNy4Ufn8k08+EVlZWcJmswmTySQGDBggHnroobDBqMeOHRMAxKeffiqEqBuw2tjj2LFj0T3AdiQS51oIIR5++OFGz/Wrr74avYNrRyJ1nqurq8V9990nEhISRExMjLjuuutEfn5+FI+sfTnbeW7qz2WfPn2UdRo7zw899JBISUkRer1eDBgwQPz5z38WsixH8cjan0id64MHD4obbrhBdO/eXZjNZpGZmdngEu+OhmNqiIiIqFPg1U9ERETUKTDUEBERUafAUENERESdAkMNERERdQoMNURERNQpMNQQERFRp8BQQ0RERJ0CQw0RERF1Cgw1RERE1Ckw1BAREVGnwFBDREREnQJDDREREXUK/x+8QXeG6v2BzwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all done evaluating 2020Pres lean for county 24\n",
      "nTrump and nBiden districts were 0.6660706583361472 10.333929341663854\n"
     ]
    }
   ],
   "source": [
    "print(\"Time for some debugging, as eval_postedOhiomaps reports low Dem seat totals\")\n",
    "nTrumpDistricts = 0\n",
    "nBidenDistricts = 0\n",
    "c = 24\n",
    "for t in range(len(HDTLno)):\n",
    "    if finalHDTLcountyNo[t] == c: \n",
    "        nTrumpers = 0.\n",
    "        nBideners = 0.\n",
    "        LIST = ast.literal_eval(finalHDTLlist[t])\n",
    "        for tt in LIST:\n",
    "            nTrumpers += vtdTrump[tt]\n",
    "            nBideners += vtdBiden[tt]\n",
    "        if nTrumpers > nBideners:\n",
    "            nTrumpDistricts += finalHDTLweight[t]\n",
    "        else:\n",
    "            nBidenDistricts += finalHDTLweight[t]\n",
    "        if t%100 == 0:\n",
    "            for tt in LIST:\n",
    "                plotPoly(tractGeom[tt])\n",
    "            plotCenter(t,tractGeom[LIST[0]])\n",
    "            plotPoly(countyGeom[c])\n",
    "            plt.show()\n",
    "print(\"all done evaluating 2020Pres lean for county\",c)\n",
    "print(\"nTrump and nBiden districts were\",nTrumpDistricts*99, nBidenDistricts*99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51c136d3-e43e-459e-b052-d26c1ae309f8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "09dce7aa-bc79-4d0a-8a58-46b3eb3d09b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"We will do voting by election in the eval_postedOhiomaps ipynb\")\n",
    "# NONE OF THE BELOW CODE IS (YET) USED.  Since Franklin and Hamilton have good HDvGOP vs. county_vGOP, do not patch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "483d95cf-64fc-487d-b4d3-ad03e215da64",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af4a433b-d805-43de-8e44-2f5ec8527a0a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"This code block creates the HDtractList for each OPEN RANGE HD\")  #ready to debug 10/15/22 if we want to run it\n",
    "# The idea here is to ratio up (rarely down) the radii in the original HD shape to capture whole tracts, avoiding banned counties\n",
    "# might lead to discontinuous districts.  May be better to start from Athens code, then add neighbors-of-neighbors if needed\n",
    "openRangeList = []\n",
    "snappedToler = 0.03 #adjustable - relative tolerance in district pop after snapping to whole tracts.  More slop as legislative are +/- 0.05\n",
    "snappedTolerPop = snappedToler * avgDistrictPop \n",
    "for t in range(nHDs):\n",
    "    if HDtype[t] == \"openRange\":\n",
    "        openRangeList.append(t)\n",
    "nPrint = 100\n",
    "for t in openRangeList:\n",
    "    if t%nPrint == 0:\n",
    "        print(\"working on openRange tract\",t)\n",
    "    # ADD A LITTLE CODE HERE TO DEFINE THE PERMISSIBLE TRACT LIST ...\n",
    "    # HDtractList[t] = snapHD(t,avgDistrictPop, snappedTolerPop, PERMISSIBLE_LIST,tractCP, tractPop3, angle, currentR, MAX_DR)\n",
    "        \n",
    "    HDangle = [HDangle0[t],  HDangle1[t], HDangle2[t], HDangle3[t] ]  #starting angle - pull from simple HD1 shape for this tract's HD\n",
    "    HDangle2 = [HDangle1[t], HDangle2[t], HDangle3[t], HDangle0[t] ]   #ending angle for this wedge\n",
    "    HDradius = [HDradius0[t], HDradius1[t], HDradius2[t], HDradius3[t] ]\n",
    "    wedgePoly = [dummyPoly]*nWedges \n",
    "    snappedPop = 0.\n",
    "    MAX_DR = MAPdiagonal\n",
    "    Rratio = 1.0  #ratio of radii to original HD1 radii\n",
    "    snapLoopNo = 0\n",
    "    maxSnapLoopNo = 15\n",
    "    while abs(snappedPop - avgDistrictPop) > snappedTolerPop and snapLoopNo < maxSnapLoopNo:\n",
    "        snappedHDtractList = []\n",
    "        snappedPop = 0.  #reset this loop's county pop ensnared in the HDpolly\n",
    "        if snapLoopNo > 0:  #we don't start bisecting until after the first loop\n",
    "            dr = MAX_DR * np.sign(avgDistrictPop - snappedPop)\n",
    "            Rratio = max( 0.5 * Rratio, Rratio + dr)\n",
    "            MAX_DR = MAX_DR * 0.5\n",
    "        HDpolly = tractCP[t].buffer(0.001) #seeding this loop's expanded or contracted HD1 polygon shape           \n",
    "        for nW in range(nWedges):  #draw each wedge, add to the HDpolly\n",
    "            cR = Rratio * HDradius[nW]\n",
    "            outerPt2 = Point(tractCP[t].x+cR*math.cos(HDangle[nW]), tractCP[t].y+cR*math.sin(HDangle[nW]) )\n",
    "            outerPt3 = Point(tractCP[t].x+cR*math.cos(HDangle2[nW]),tractCP[t].y+cR*math.sin(HDangle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([tractCP[t],outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        for c in range(nCounties):\n",
    "            if c not in bannedCounties[t] and HDpolly.intersects(countyGeom[c]) :\n",
    "                for tt in countyTractList[c]:\n",
    "                    if HDpolly.contains(tractCP[tt]):\n",
    "                        snappedPop += tractPop3[tt]\n",
    "                        snappedHDtractList.append(tt)\n",
    "        if snapLoopNo = maxSnapLoopNo:\n",
    "            print(\"Somehow, we did not converge on snapping HD\",t,\"to whole tracts. Final pop vs aDP =\",r3(snappedPop),\"vs\",r3(avgDistrictPop) )\n",
    "        # Now, finalize the stats for this snapped HD\n",
    "        HDtractList[t] = snappedHDtractList\n",
    "        snappedHDpop[t] = snappedPop\n",
    "\n",
    "print(\"All done snapping\",len(openRangeList),\"HDs to whole vtds from original HD1 shapes, barring banned counties\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "dd30e1f0-be6d-4906-ba56-5cb9c17f6400",
   "metadata": {},
   "outputs": [],
   "source": [
    "HDtotVote = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    for tt in HDtractList[t]:\n",
    "        HDtotVote += vtdTrump[tt] + vtdBiden[tt]\n",
    "    HDtotVote -= (1. - partialTractUse[t]) * (vtdTrump[partialTractNo[t]] + vtdBiden[partialTractNo[t]] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "65e442fb-b9f0-43e8-9d5a-7465c005d07c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now capture the unpatched HDpiece results in a csv file\n"
     ]
    }
   ],
   "source": [
    "#start 9:02, end 9:46\n",
    "print(\"I will now capture the unpatched HDpiece results in a csv file\")\n",
    "date = \"14Sep\"\n",
    "#write another file - tract Use and GOP lean - don't write the geometries\n",
    "tractNo = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\":tractNo,\"centroid x\":tractCPx,\"centroid y\":tractCPy,\"tractPop\":tractPop,\"HDwt\":HDweight,\n",
    "                    \"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDtotVote\":HDtotVote,\"HDvBlack\":HDvBlack,\"HDvHisp\":HDvHisp, \"HDarea\":HDarea,\n",
    "                    \"angle0\":angle0,\"HDtractList\":HDtractList,\"partialNo\":partialTractNo,\"partialUse\":partialTractUse,\n",
    "                   \"vtdBiden\":vtdBiden,\"vtdTrump\":vtdTrump,\"neighborList\":neighborList} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HDpcL\"+str(levelL)+\"lLmAR\"+str(maxAngleRatio)+date+\"unpatched.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f34423b0-b0d1-475b-8ff3-f6d31e599226",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now compute the tract usage\n",
      "and plot the tract usage distro\n",
      "1.0 0.21141 = avg,std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now compute the tract usage\")\n",
    "tractUse = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    for tt in HDtractList[t]:\n",
    "        tractUse[tt] += tractPop[t] / avgDistrictPop\n",
    "        if partialTractNo[t] == tt:\n",
    "            tractUse[tt] -= tractPop[t] / avgDistrictPop * (1. - partialTractUse[t])\n",
    "print(\"and plot the tract usage distro\")\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * HDweight[t]\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 * HDweight[t]\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "print(r3(avgTractUse),r5(sdTractUse),\"= avg,std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=HDweight)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a5103a4-8d68-4dc4-82c3-9938749f4cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#to add in here - calculation of each Home District's lean based on a partisan lean model (by election year)\n",
    "#for pie-cut HDs, technically we could create ghost HDs of their other pie halves, and track the R-D lean of them\n",
    "#before doing that, let's see if it matters -- do the # of GOP vs. Dem voters across all pie HDs match the county partisan vote totals?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb726d1f-6f9b-4e35-ac79-c7968d7b120a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c83d973e-a9b0-4e76-94a0-bd6904a59a87",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a94dc37b-2032-44ee-a452-d7c3c65b7ebc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "ef1b7278-b0de-4bbc-b91f-111e82c9fe5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.0019 0.43251 HD avg vs true 0.43061\n"
     ]
    },
    {
     "data": {
      "image/png": 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1nATWJlnX8VhJkq7Q5T2o9cD5ge1Z4NYOfdZ3PBaAJPvozb4Ank/yVIfahuFm4LlleqxR4ZgtnWO2NI7X0i37mOVPhvZPvWa+nV0CKvPsq459uhzb21l1BDjSoZ6hSnK6qqaW+3FXM8ds6RyzpXG8lm4Ux6xLQM0CGwe2NwDPdOyzpsOxkiRdoct7UKeArUm2JFkD7AWm5/SZBu7sX813G3Cpqi50PFaSpCssOoOqqstJDgAPARPA0ao6m+Tufvth4ASwG5gBXgDuutqxN+SZXLtlP604AhyzpXPMlsbxWrqRG7NUzfuWkCRJK8qljiRJTTKgJElNGpuA6rBc0y/1l2k6k+RzSW5ZiTpb0mHM9vTH67Ekp5P89ErU2Yquy3ol+ckk30jyruWsr0UdXmNvTnKp/xp7LMn7VqLOlnR5nfXH7bEkZ5P843LXODRVNfI3ehdo/CvwWnqXvn8R2Danz08B392/vwt4ZKXrXgVj9iq+9T7m64AnV7rulsdroN+n6V1Y9K6Vrrv1MQPeDHxipWtt5dZxzNYCjwOb+tvfu9J1X+ttXGZQiy65VFWfq6r/7m+epPeZrXHWZcyer/7/AOCVLPAh7DHRdVmv9wIfBZ5dzuIa5VJoS9dlzN4NfKyq/h2gqlbta21cAmqhpZgW8mvAJ29oRe3rNGZJ3pnkSeBB4FeXqbYWLTpeSdYD7wQOL2NdLev6//INSb6Y5JNJfnR5SmtWlzH7QeC7kzyc5NEkdy5bdUM2Lt8H1XnJpSRvoRdQY/1+Ch3HrKoeAB5I8ibgj4G33+jCGtVlvN4P/G5VfSOZr/vY6TJmnwdeU1XPJ9kNfJzetyaMqy5jdhPwE8DbgFcA/5TkZFX9y40ubtjGJaC6LNdEktcB9wO7quq/lqm2VnUas5dU1WeSfH+Sm6tqHBf57DJeU8DxfjjdDOxOcrmqPr4sFbZn0TGrqq8O3D+R5INj/BqD7kvPPVdVXwe+nuQzwC3AqguocTnFt+iSS0k2AR8D3rMa/9K4AbqM2Q+k/9u2/yWVa4BxDfZFx6uqtlTV5qraDHwE+I0xDifo9hp79cBrbAe931nj+hqDbsvH/R3wM0luSvKd9L5B4ollrnMoxmIGVd2Wa3of8D3AB/v/Hy7XiK0MvBQdx+wX6K3B+H/A/wC/OHDRxFjpOF4a0HHM3gX8epLL9F5je8f1NQbdxqyqnkjyKeAM8E3g/qr68spVfe1c6kiS1KRxOcUnSVplDChJUpMMKElSkwwoSVKTDChJUpMMKElSkwwoSVKT/h8jNNkQYSqfcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "9db8f486-ca70-4150-8636-ffbdd4a9d07a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the correlation of HD area to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and here is the correlation of tract usage to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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bdhwOwlz7+mtnXEmxAsJSdUZCQtRgqES8fymTbqk+mHJ/X+U4Xy0+Q627enht99FsmCvQNKahmkMqmYqV2qgGttTG8GWk+CCSMBSfVWsQSZ2jpY6p3J8hyfmK7VNLz1BcORNH4NZLmpkxYXRNjFFTqNSGFRCWYUktTQZJMEsuaJt0pf0tSa85HHxAw2GMJmYdKfBCXOvrHFCKtKtq6jNUpRaTxVIphttkYY7XESHjqlCyVKXGrk1Hd6/fXtDcNBx8QMNhjCZL502kLuXQl/ZyTESguWk0Ow69P2w+A9hMasswJEnGci1hjtdVXr5BSiCVctjjh2UWYrCZwsWyxcuZTV4phsMYTRbNaeKmRbPQedSugu0HPeHgDJPPAFaDsAxDatEhWYjoeFdcu4CO7mM8snk3D73SydoCYZnl0JaKOcGHqijeYMyCw7Fw340LZ7G2rSvshwA+fs4kvnn1eTlmvlr8bFZAWIYdw22yiBvv3eu3k3ZVUXPDYEwr0Umn0HGVLopXLkFX69+1if7e733+HX7x5n6UgoZ6J1Y41KrJ1AoIy7Ck2pNFqSu+6HiTakED1ZZqbdIZbj6EcvLc2wfJKEg5woprF5RUgqXaWAFhsRgkDbestNmn1P2i1NqkM1RmwVoz1axt6woc1RlX0dF9DAiPs5ZNplZAWCw+SSf+UiffuEmrlIlsINpSrU06Q2EWHEqtKen3F00iUHnGWasmUysgLBafpBN/KZNv3GQAVHwiq0U/jVl7ynxdLoZKaypFEN24cBaP+m1d61PCjQtnxY7z61eeUxPfURQrICwWn6QTfymTb76Q3KGYyJJoHuXKoB4q01whhkpryieIzHug92sa08AV86ew//gH3HJxc/B5a0m7K4QVEBaLT3TiBy8jNl9oaJLJLd+kVQsTRKkTdr79K2WaK5Wh0privlPzHogIIpDJqJCJ6c297UE116TPWbWpmIAQkQeAa4EDSqmWmPevAJ4A3vU3rVVKrfTf+wzwQyAF3K+U+kGlxmkZeQw23r6USS/J+eImrVow/5Q6YUf3X9vWxYYdh+k+erLspjkY2Pc4FNFt+cKW9T0gT/mifqPXd7mfs0pRSQ3ix8A/AT8psM+LSqlrzQ0ikgLuBj4FdAGbRORJpdQblRqoZfhRyNRRjh9cOVe7cZNWvolsKKNw8q2E810/1Jwn5fDI5t2kXUWdI9SlHDKZgZnmotds3dXD2rau4Py1OHHGhS07IrgFats5Qs69qbVosygVExBKqRdEZO4ADr0E2K6U2gEgIg8B1wNWQFiAwkKgXD+4aoRlQuWd1yZxpo5C1zf333P0JA+90omrvPDNWy6ZzcwEVUqjE2v0u1xx7QJWPt0Ryj6uxYkzyqI5Tay8voUVT7Tjuoq6lHDF/Cn8cusBMhmFI/BXn78w57PvOXoykXCtFtX2QSwTkdeAbuDPlFIdwExgt7FPF7Ak3wlEZDmwHKC5eXi187MMjEJCoFwTezXCMm9YOGvIV5PmhJ2vqF9cRrZe5ev7fOPCWWXR1J5p30tfOiscdBXUWps447htSTPzpzXmaEPFNN06R/jiJc3cMMB7WEmqKSDagDlKqfdE5LPA48C5ENS3Msmrtyml7gPuA6/cdwXGaakxCgmBUib2YuacStuzo5OjUF3ndTHnq6lVmPe5aUzDgENXo9e8pmU6m3YeCcxYNy2aNWDhUw1MAaodz1+/8pyc/czvPuMqZkwYXZOfsWoCQil13Pj7ZyLyzyIyCU9jmG3sOgtPw7BYgGTF56BwvH0tOAejk+MNC2dxgx8nX42iecWcr2aYrpkFPJj7GHfN6Cp8uJHk2aq1RMZ8FBUQIjIG+FOgWSn1VRE5F5ivlHp6MBcWkWnAfqWUEpFL8AodHgaOAueKyFnAHuCLwG2DuZZl5FFodb9qYycrnmgn4ypG1cf/QKvlHIxOznGCbrDjKGTWKOb8jXO+mhNZ05iG0OR3o2EW60u73LXu7ZxidMWIXnMoIpEqReuuHu5a93ZOtNeati4O9Z5icuOowJRUC5FsxUiiQfwvoBVY5r/uAh4BCgoIEVkNXAFMEpEu4HtAPYBS6h7gJuAPRCQNnAS+qLz2dmkR+Qbwb3hhrg/4vgnLaUzSchWtu3pY8UQ7ab+V16n++EmrGiu4QuaaSl8jrgVmEsEYnciiglUBdSnvProKXtp2iE07j9Rc1NFQEL3Hjng9Px7e1IlfjgmAhzfv5uHly4aFIEwiIM5WSt0iIrcCKKVOikicnyCEUurWIu//E14YbNx7PwN+lmBsltOAUspVbNhxOBRqqIBfbc+dtKqxghsKrSXfNfT2gTh/oxOZKVhbZozn0c27g/MORae8alLIb2XeY933YfaHxrBqY2dov3RGsaata1jcnyQCok9ERuM7ikXkbOBURUdlOW1IEvdfSrkKrRnoCppKUTMx5pXUWsz+03HXiOYwDNT5G6dRaG1Nk0oN7LPVWiXWKNHWsSuvb+G2JdnIyej3+82rz+PnHftiz1V0hV0jJBEQ3wP+FZgtIg8CHwe+UslBWU4P4mLge0705UwQpZSriEbXrHy6I3ZCroaTulJaS6H7CNkyDuW4dtwkHk0Qu2lRvOApJACqGTSQVDCZCxVXKVY8kS2dAfHf78qncq3jKUe4YeGsin2eclJUQCilfi4ibcBSPMH3R0qpQxUfmWXEY/7g+tKul2Skcp2n+SbWfGGWpkkkX0RMtZzUlbA7R+9jz4k+vn7lOaza2Ml3H99CRkFDSli9fFlsyGVS8k3iZoJYQ71ndorWFiomADbsOBzY7vv6hzZoIKlgimZLu67KGWf0+x1V54TOIcBfXd9SsgCtFkmimD4OvKqU+hcR+R3gOyLyQ6XUrsoPzzKSMTUDESHjqrw27LiJVb8ulv0LuSGvpZaZGEpKHUfTmAa0lcdV3uvWXT2BcADoG6TdOy46R39HZoJY05gG7nwqq7Wt/mrWN6SPPdXv8qc/fZXll50dmGiaxjQEfgzXf10pzPtbykIhThgWK/Xe1tkT2nb9R2eEzFLmvtUOu44jiYnpfwAXichFwJ8DD+DVV7q8kgOzjHySmoMKUewHXizRa01bFwJs3dfLyqc7qv4DHchE0XOiD4HAOdpzos931of3G6jdOy46x/yOzAnX7KDWl3a59/l3uGj2hMA3os+x8/AJvvPYFsDLQO450YcjnoBzxPsMlSDOHFeKXyguWzp6/rB/Jnz8k6918+Vlc2sm7LoYSQRE2s9VuB74R6XU/xSR/1DpgVlGJnFlG4qZg+KO0xRz/Bb74ekJTZsOqv0DHchEsXTeREbV596D+pTQ56sQ9amB273jonO+efV5AHznsS082tpFOuNNuBfOHB869tk39rPuzf3BZHzfC++w8/CJ4P1n2vdy25JmmsY04Ign5hqGsJdDz4m+kn0z+cyE0fIZV8yfEghujau8Z06HHpsJh7WYOJdEQPSKyH8Gfge4zK+2Wl/ZYVlGIsVWx0l+eEn9E5pCPzxzskApHEcQVFV/oAOZKPLdg9XLlwUa0mDq/MRF5wCxeRU9J/pzjjd9I8svOzvQHACuaZlO664eVj7dQcZVpBzhK8vmDrh0R6mfxVyoDJaQLyij+Pkb+2P3O9h7ati0HU0iIG7By2T+faXUPhFpBv6ussOyjEQGqkYXO66QYNmw43Di6Kh8+w0l5Yh0Mlem3//ChYM63jTHmVVftT/CzKtIpRzePfRe7DkdkdDneaZ9L9e0TOe2Jc1BOQ+FV5foRy/uQEFFTH2F7u9gfVD6edJCUxFv1pvUOCr2ma7FtqNJopj2Af+f8bqTwj0eLJZYBqpGL503kTpH6M94K8wkxyWx5Q9FstxQOL5Dpo2UA0oNuI9CIZ+NNotE/RF1jnDz4tkoYHUkKQy891cakTu3LWnOyR+oSzlZIeFLncGY+lZt7AwJIZO4BUU5nMSmX+vR1i4yGS/nJJ1xQ/6g7ft7Odh7KqfMd60ESZjkFRAi0kvYfKaAQ8B64FtKqcMVHptlhBE3ISf+Ufj2aYon8QPJtZVKhJ1qBjLpDOSY6GeF+IzmgSYlxoUHm/6Ia1qm03Oij6YxDdQbSYp1KeG3F88OJeTl69t8wbRGXus6FlynlEzvKKs2dgZmrBe3eRH5cZFDST93KRO3fp5uNIourmnrCmVTv7LTi2xKCbTMHM8tF3tjG1ZRTEqpxug2EWnCS5K7B7i5csOyjFTMCbmUXsbpjL+6zCR33Fbb6TcQk9pAndRmljRKkXFVTqRRkntd7L5F3584toG/fHwLSsGoeoc7r1tAR/cxFORkasdpOv1+32Yt9h2grm5wZb6fad+b8zoqIFp39YT8M/nCnrU2oJ3wSSfu6MLj0c27g4ABTUbBa13HaN+zhbMmnxloZcMtiilAKdUD/DcR+XKFxmM5jUg6GQ7Ucbvi2gWBmaEaP7aBjLscTmogZ8W7pq0r0QSUxOx248JZKGDcqDrueWFHsP1Uv+eI/ps8fo98mo7+vyPZCKlSzWLmeKM65uj6FK27ekILk1vvezmYsB9p7WL1V5fm3MOBFDfMi9aAY8go2H7A89041FaDpJL7QYhI/UCOs1iiJJ0MB+Ir0JExfWmXTTuPhEoiDBUDGfdA/SLRFWt05f5oa1cwPRXz4ySNJps/NWxkEMntuWwSp+mkMwoXTzg0+BFSpQqHW3+0IXiGPtsyjRe2ZQs9CLDuzf28sO1gqKBjv7Gaj3MSm45zfZ6BTtxaA07Cx88tXUBWkkI+iBtiNjfhRTU9WrERWU4bSpkMS/UV1EriUbFxx9m3B+oXyWcrNycoAW5ePHtA54/e06njzgCyfoPPXTQjNiqoaUxDEB2mtboF08fReyqNAlpmjB9w9Ni9z78TSsxb92Y4tNSPYKYvHS7oGMoRKWJKS/lO+IGGCpuZ7oVIOVJTwgEKawLXRV4rvIY+P1RK/UvlhmQ5naiUk7gWfBDFSFqscLDnit6LgSbMRc9zx+VnM2/SWO57cQeugqdf38slZ03ktiXNwXg+6M8KpvqUgAjpjMuL2w4heH6LJL6GfL0/fvHWgdB+Z9SneO9UJud4HWYL3jNXLEeknBFuSbLCRfLXaKomhZzU/xG8WkxKqV+Z78Vts1hqiaEIYR0socSq/vzFCks+Vzr3XOW6FzcsnBWaVDfsOIwu5Jp2sxVOdfE9jcJLHhNUyb0j8jnYvWuHl+aH3sudjAVCYbaQP9Q1X5b/QIiWYNeaTsqBRc1NtHYexfWTA6Olw2uFJL6E/w4sTLDNYqkpKhnCmo9SQiKjxQoHU+qjWOHDwSZhRSdprYUsnTeRlCNBTwhXqeDzm9vBC+s08wKSOmTzmQujiWn5+NSHpxadfMtZLC8u+umzLdN44tVub5wKWjuPknFVkCNSi8IBCvsglgEfAyaLyJ8Yb43DawVqsVgMSp1kTC1noMUKK3Eu8/MUq3q6aI5R4VRl6ygtmtPE7ZeeFYpyWjiniS/81qwgZyKpOS2fuVBHqpktZjWOeL6H+pRwx+VnF/2spfqsCvX9jkY/9aVdnnytO3id0Y4RvJLhlSpMWA4KaRANwJn+Pma4wnG8ftIWy4gg6aq/2H7RSWZtW1fR85paTqFihUko57lKqXqar8Jp4+j6ULG6TTt7+E3n0ZJXzIXMhT0n+kLNigDqHFh5/YUl+XNK8VkVWgiYiYTgmbccX6OLw0lYGaBaFPJBPA88LyI/tr0fLCOVpKv+JPtFwzgf2by7pJIX5TSJDfZcpVY9jbuerjJrrqZNP0Upwjjf51k6b2JOR7tPnp9rUiom3JP4rPQ59hw9mVfb0GVhdISUCJw75Uy27u/FlGMCge/BdLrXms8siQ/ifhG5WSl1FIJs6oeUUv+uoiOzWIaApKaFJPuZk8yeoyd56JXOqofZJiU6OZWj6qm+H2vbunjolc6gxlImphObOQ5tv9chpnEah+kAvvL8Kfzizf1BKOlzWw+EEuNWbexkxRPtZFzFqPr8wjr6+aJlQcws8DpHcrLV9TluXjybVRs7UXiVbN/c15tzrY/MGs+K6xaEhMOwKrVhMEkLB/CyqUVkSrGDROQB4FrggFKqJeb9LwHf8l++B/yBUuo1/72dQC+QwetHsTjBOC2WkklqWiglqW/RHK/G1Nq2rpoOs9Xkm5yS1M1KsjJfNKeJRiPrWhHfMS7Ofh+nccQVDDSzp00B1LqrJ+SjSNrONM4prxcImYzLFy9pZsaE0bGf+4aFs0KZ63FMHXdGqKR5reTtREkiIFwRafaruCIic8iXMx7mx8A/kb/y67vA5b7AuQa4D1hivH+l7X1tScpA1fOk4bClhs1WMsy23KaIQg7oQitcSF5grnF0fdGOcVH7vUZHRul9un0Tjwre17Z+QBGqpXTXurdD9v+kNv/oPRHIySUpFCYbpzmBLosuPLf1AOve3B9Uwl0wY3xN5u0kERB/AbwkIs/7ry8Dlhc7SCn1gojMLfD+r42XG4CBZe9YTnsGq54nNZ0MxMRS7lVgJUwRSbSjOCECJF716o5xSikckVgNIhyqCwpv/4Y6h6YxDaFubXUph3TaxYWsI1x5dv0V1y5g677ewKykiwFGbf6l3JMbFs5iwYzxeWt7xX0vNyycxSOtXWTSLo7AYv+Y7Qff48j7XmOlvoxi1cZORtXnT5Sspm8iST+IfxWRhcBSvPv8xxVY2f8+8Ix5WeBZEVHAvUqp+/IdKCLL8QVWc3NtxhJbBk+hH0mtqueVIElZ6mIhpMUaAuVzBscJkSSrXrNjnMIzG618uiPHUR0dBxBkO7d3H8uaeFzFLZfMZuaE0TSNaeCZ9r38avshXAVKKTq6j/Hwpt2BWUmAS8/NliVv3dUT3MukTmugYG2vfAJUlzhxVbbMdxSFJ2ifad+bU2qj2r6JpEX3MsAB4Azgw+KtBF4oxwBE5Eo8AXGpsfnjSqlu39fxcxF5K9/1fOFxH8DixYuTmL4sw4xiP5LhUFajXEQ/a9OYBu5evz3IfdCTlC5joe+VKTz0fnENgfKRT4gUEizRqB/zx5nPFxA1a+me4VHHsFmeY/60Rja+eySIHlMQMiuJwILp44LPnbSpkjkWXbwv3yIk7hncuq83J8IqSsrxtCRXwUvbDrFp55HYsNlqLX6KCggRuR34IzwT0Kt4msTLwCcHe3ER+QhwP3CN2YBIKdXt//+AiDwGXAKURSBZhh/FfiSVtPfXGuZnNSd7x8jEhmwZi7VtXaxt6wpCbp2YLOsk5ox82/MJlmjvB+1/0LiEHdVx5ze/90KO4a37esn4/UJQipYZ40PhtUrB/S+9G8pUN+9Rkkm32CJEfy9a49m6r5eVT3fgKq+UBkoRaQdBSrz6S8+07+WlbYdix1PtxU8SDeKPgIuBDUqpK0XkfOC/DPbCfm/rtcCXlVJvG9vHAo5Sqtf/+9PAysFez1JZKmknTfIjqYS9fyAMhb1Yf1ZzVauUQiRrj3fwylo8vKmTdKjStApWrdGGQqYgMVfW+RzUpvml0OSeTruI9iAbtHcfC64dpyFqvwWoUJHBtW1drGnr4kb/9Yon2rNtSjOKZ9r3BlVjtenJdRWOIwgqp6mS1sJKDVKI+661xuNItsyIKMXUcaPYd/xU6JyOI8yf5uUgv/zOYdw8YbPVXPwkERAfKKU+EBFEZJRS6i0RmV/sIBFZDVwBTBKRLuB7QD2AUuoeYAUwEfhn8dpI6nDWqcBj/rY6YJVS6l9L/2iWocIMO6xE4bFq/0iSMtAWo3E29wUJSmBrwZkVEl7/54+dPZHOIycYXZ/KicGPqxobrbwKXvOftW1dsSGYeoLWDmNdodX8zHG1oaIc6vUmzHz2e+23MB3P3/Udz+B1abt58eyQCUcBv9rumWpWXLuATTuPBAsL83Pr60ZNbiuuzd8Rr1hUl/k5VGRMUeEAkM4o1vganqs8Abbi2gUAIYFVzcVPEgHRJSITgMfx/AE9QHexg5RStxZ5/3bg9pjtO4CLEozLUiPoyp3aAZkkU7ZUBvMjGYpVvQ6pLNa1LW/ylSO4eJOGyRl5Erv0eb6ybC7/2rGPXYdPBLZ3s2GOyUWR5CyNnthMFPDI5t2x7Ti1U9VV+I13VMikZYZ66kk4rl6SfhWnIZohr6bj2RQ0fRnFgd5T1KWc0Ph1RVutSeSr/RTVwvr63RwBtHr5sthnJk6omZ8DyDEpRalLCWLcS0GxfuuBYAwNdQ6rv1rdhLkkUUxf8P+8U0TWA+MBu6K3BOSr6FmJB7vUyX4ookDMa2jzTpwprFDyle7NHMV05sY5ms051/GzxUIlHYzcgDjhAPHlKiCbcPb1K8/JiejRSYApR7yFQcZbAUdNVF+/8hwAOg+/HyrcBzClcRSQX0OMCqU4LeSXbx1gYfMENu/sCd0/0+m74toFWQd1pPlPVNMxhVh/Jvc5jpbwjmaa68/x/NYDeaOWNApoHFUXmNFSjoQywvvSWS2uWhQUECLiAK/rTGi/PpPFEmLRnPiKnuVmIJP9UESBmNco1Fc5OpZDvaeCyaHOF7DROdBxvJyBv3hsS6yjOdgP77oTxzbw+KtZBf+OT8yjcXR9Ufv6yutb+O7jW4JVryNhIRfV4Ezt4M4n2wHPzu+qeOdv4+j68OcSQo2LouePOuM7uo9RnxLSviDVnz3jKjbv6iHlgOt6zm+NHscz7XuzWoKfd7CmrSsnY7xpTAN3PtURaCP1qXBSXZIGT/pz7Dl6sqiASGcU9724A6W877l54tigN7X5GapJQQGhlHJF5DUzk9piiSNfRc+BUKh1ZqmT/VBEgUSL9M3+0JjgvVUbO4PkqqXzJlKX8vZzHC+bVtvY7/ycV43mLx/fEgiJlMDtl57Fyqc7ImUbvGN0VI7gmSuuaZkeTNYC3HHZPL792QsSfYb50xo9R3JGkXLgloubAxt8obaod6/fTtrNTtpxDvDgHhltPh2t7pBrdjP/3nP0JD/8xTbSGS8a6qoPT2H91gMhU5xSnjmnaUw9PSf6c/pIX9MynU07j2Sjmsj6WMzrLZrTxPxpjaxt64r1QcQVMPz6lefQuqsn8Bno/VpmjKchJXk1Q43+rjOuyhEOdQ6BI75aJPFBTAc6ROQV4H29USn1uYqNyjIsKcVPUKyefpyWMJDJfigc3PoaOgrooVc6WdvWxVeWzQ3MKi9uO8TXLpuHq1RgktETh1IqmGyiE1Rc+Qm9em3vPuZpFRkFIqzfeiCYgBVw/FQ6NM5CiXRr2rqCSdfP7cobwZQv/j/lCFfMn8KkxlG0zBgfqjW0aI5XxO7Bjd46081ky2eYfhjt8K5LOWQybsiO3592OXD8A9wYU5MCjpzwspMdIceUNH9aI2v870dP2g9v3h0btVUsKMB8/swADUdAHMH1GwFdMX9KkMXdc6KPTUU0CvA0wQtnjWfBzPGJWrFWmiQC4ky8onsaAf62MsOxnA4kqacfpyUMdLIfiigQHe2jJ58P+l1WvRJWuh9/dU8wCZtTXMqoD7R1Xy+dR06EyjmY5aN1RM9tS5q5e/32wNSUybi8e+h983JBlBDEF7gz771AzrF3r9+eU9o62uPCjP9/ZPNunn1jP45449Qd0/REvWDG+OD8LvD81gPh82dU0JA06jTX96yj+5hXZsOXYkrlmmHiTHzmM7Dar7RqaiFJgwqiLVfvXr89uKeZ4D+eKevZN/YDWU0mN9A3S8r/Agr5iqpBEgFRF/U9iMjoCo3HchpQSAgkSUgq5cdTyKld7uimpjENoQng+AfhFXzc7CDABdPHAZ456juPbQE8jeO5rQe44/KzQ+Wj8SN6vvPYFg75ETyZjHevzpoUtmFP8p3AkFsIL3rvdd0gz0wmPPf2Qa+YnJHBnK/HxaI5TZ7TOqOCc7vGRPngxk4e3rSbT54/JTRJvrKzh9bOo8H5oXjkT8aF32oez2/8lp2Ok9V4NHG1kjQ3LpzFo5t3BwLXJFofSpcJd5UKsq/7M555b8GM8YGTO9paNYqO+opiBhDceV18HaZqU6jl6B8A/wmYJyKvG281Ar+q9MAsI4ckvQY05TQJFdJUBpqzoM0/LTPG0959LLSa7PATv/Kx/71T1PkTmjY9KOD1rmN86f4NzJ/aGNr/2Tf288K2g3xl2dzA5xBNfqtLCV+8pJkbFs5i675efu6vWvHHGI266ev3CtzFOaFXf3VpUC11td/LwsxgLtTj4kBvbpy/SdpVrHtjP9FldMZVXPXhqUxqHMXDm3aHQ7AM9GHK/x605uDmzruxlWLNZ/CK+VOC1X0wDuX5fzoPv8+3P3tBTpnw/nS4BPl3H98ShHLrAI1o4EAxHDxNylWK9u5jNWFSilJIg1iFV0DvvwLfNrb3KqWOVHRUlhFD0l4DJuUyCRXSVEp1eK/a2BmKkTf5qZ+wdajIJImCW/zJVheZM0ssjKpzcg7pS7vc/9K7gTP78vMmexOtTyajmDFhdGDiMktqt3cfy0kCK9QPWt933bQnWto6X4+L1l09PP/2wZyxnzN5LDsOvR84Yl3/HsTcFnYfORHrWwBvIl08tymICjJ3Szm+xmJsi2oC0WfwE+dOjr2Oq+CeF3bQPHEsPSf6cuo5icpGSWWU57dZNKcpCNDQjY7SmWz4sUNWsMVdT5u6Vm3sLJh3US0KtRw9BhwDCia8WSyFSNJrwKScZp98mkrrrh72HD0ZMs8Ucnjr1WS+vsL9GcXqjZ3Up4Q6PxRTgE+cO4l3Dr5H99EPAK943g2RInNmpu85UxtzQiPN2HzXVUxpHEV9JBpIT4jRz2smYZlRN8XIJ8Dzbd+w43DgE9A0pITfu3QeK57YQgHrCymB598+SNqvpaR9IeYhE8bW85vdR3OOFWBhcxObdoXvWVSDMJ/BU/1+bwf/HvplkkLX01VV6+uyCXiOwMI5TaHvx/Tb6Of5xoWzuGvd24HgB88f4+pwMx9tsjJNXX1+ZvWwEBCW04dq11Eyx1HI7DOQQnJx9XP0NQS46oKp3HH52QU/94YdhwtW5ASCRK5PXjCV9W8dwFWKV/zKnPoccff3xoWzgoglyNbyCSZV47ouninnzs+1sH7rAX751gFcP3MdvFDjuHLZAwnxzSfAze2m+SrqSL/zcy30nOgrKBwAWmaOZ8ueY6EcEoFQNrjunQB+YyB/VhdH2OSbm0yiGsTSedk+0Qp47u2DwfiWzpvIzzv2hZL4Fkwfx6I5Tdy0aFbg0FYKzpnayKtdx0KalcZ8Br959Xkhwf+VZXODYoF1KYePzhrPqbTLsnkTeXnHYV7rypomo8EC1cYKiNOcSmcaRxORzNDH6DjuWvd2qOzBXeveDqJR8o2z2PijE525mgRY9+Z+rpg/peBnNvMXRDyhcvaksfzoxR3ZxDI8m/6UxlGhqqE6Eznu85rj1vbnB29fGlqBRifYdW/s58VtB7lx4SxcN9tfwSxvEpfQVm7hHx3/FfOn8PM39occ6QoCLY2Yekx1jnDLxc1s3d8RTKbfvPo8vvXoa7HXFAga63R0H+OhVzpjXRZRX9CiOU0hv0MmE9am9H3RSWs/fnknn1owzXNo+457cbyyGKYzGcgpta6fQfO+6wWGfia0FvJa1zGm+RphOqNyhE4tYAVEhahmF6hSqESmcVxDGsjfnjIagil4q+VfbT/Exh2HuXnx7HD9H2OcUfPBn/70VZZfdnbeYoHRshKuIlHtKF1O2gGunD+FZ9r3BiYEM3saMKKBwpVC9f02aw1FC9Rt2HGYa1qms3HH4WDFa6L9FQoSlTcx/QpxFUsH0mdaj9McP/54tCNdRzvVOZ4TfcGM8ax4sj0ILTWLOkYTLD/oz+RcLyVZbQ88M1C+iKeow7x1Vw/PbT2QPVcqV5vqPZUOTE1mbSWMvBXd+e3B25eGOtbp7wW8Z3DlUx3ccnH2+dNadFyP6n29p3DwtL+4NqbVxgqICjDQVXk1hEqpJqBiIaP5GtKYBf1ORbSDtUaDdwdonjiGziMnQqUR6uvCDWP0OKM/vp2HTwShonFCQkedmBnL5uQa/Yytu3r41qOvBZNRRsFfPL4lWLkK4IgEoZWtu3pwXTfITVjxZDsZPzvZcZyg6umKaxfkNP4xE8aiE4l3Hc/UISKMG1XHJ8+fwi/eOhC05cz33ZWifUFWkDuSvzJvNHvczAq//LzJQU2hjOs50U1Hrhn5pb+Trft6uWvd2yyYPo59xz/IuV5GwQvbDnLF/CnB86XvCxIOftI1nsyGRWZ3uZsWzcoRko9s3p31GfiC/a51b4d8BAovv+Xe59/hl28diA1tVXiawWtdW0JNm8ykySguBIEGtYYVEBUg6ao8X2XPSrYWjJvkb1w4i4O9p5jUOIqt+3pjS06bKyat5rd3Hwsid57beqBgQ5pt+3uzmcN4cf4vv3OYaz8ynade6w7eq6tzWH7Z2aHyEnqy/aIRAWSaqh68fSl/+tNX2Xn4RPA5H97UmTOxmZ/9rz9/YfB5dI/k6IR5ydwP8aLhbNSYk5H2PegWmmvauoIQVDNxKu0SxGSe6ne574V3+MqyuUGdJPOZicbo64lG27IzruKeF3Z4CVgp4eaLw6vP6HccfR51slt3JAlOb9Nlv10VX5lXn19HRenwV+Ufc+D4B9ncCUfoPnqS1l09Of4LrdFs3dcbyv/IR1+/y8ObOkPJfh8/x2sleudTHSHfQLRhUbQbnckaI4cDvKq3phCKYhbUK4T5G1j31oG8+6WEipSAKQdWQJQZM0ImnfYqRMY1aI/2UPjk+VOyE0RMxqp5XCEtI66uTdOYhmAy/+XWA2QyirqU8NHZE2jrPBrYsuMyPT2nYDgZ6YN+N/hBRzHr4LsKtu3vZflPNufEnYNnOzcLywFcft7knLBBHWm0wM890LV5zNDNzyyYFlqhdew9HkxK5v02BbCOX9c2fPM7+KDfzVs2O+czk/WZnIoxj2h0NIvWdO55YQff/8KFAHQfPekV7MuoUMG5Or8u0g1+2Q1dqkNfV6/Qzc95630v059R1KeE1cuX5az2TfNPyhFURgWr5h+ue9scMhlXcde6t4N+znEaInilsfsznp399a5j1KWEsyaNZdeRE6x+JVwcL/pdRPM/8iECb+w9njXtORKY9W5aFM5w/ovHtmQznCPd6ICQ2e/R1q7Qc7/ZcHyLf11TICjfoV4sqU/7pZrGNPBOpM6SyV99/sKa1B7ACoiyojMvvZWTX5dFxTdoN00uaVexzliVuMpbAbuKHJPArT/aEKyUorXizR+enox0rHWU/ozKqQ0Tt583CSW/Byryd1QAFOOXbx1g+U82B/V8tNBqmTE+p2hdX78bqiB7iRErr9xsrZ/oarkv7U3msz80JrAh6+9ArzSL/fijuBAKbdSYQrdpTD2ugiPvZ8MwH3hpBzsPnwhWt/OmhLOh5046M5j0tu7rzVm5Rs2Ca9q6Ag2kL6NY+VQHK65bEDhN9xw9GUTm6FWz9x27tHcfi+3Z8OK2Q7y47RACoSKBIVu9SFAmQ597+8Fs6Y++PH6j/rTL1HFn4EXU5yflCFedP4V1b2YXGspV/LxjHz96cQeu8rQprT1ETUZmLke05Ho0RFcLBwdoMJzij2zeHXxPl507OXbRo3EErr7ASwAslkDZc6JvSDP+S8EKiBIo9iWamZdpIyvI/HFooin60WgMnW1rmmnuff6dQO3tS3u20Ct8h+mC6ePo2Hs8+OG5pc5wNULGVaEfnp6UDp1/KqdonUQmqwl+uKWrVI5dX58HN9sg3qwoCp5gPnvymTTUOaHQw2JMOrOBQ+/1xQrYC6Y38sZer6vbofdyM3zNRLJ02mVsQyr0/vYD7/Gl+zfw4O1Lc+L7HYEV14br9kTDJF/zs7R1b4agZAdhYZ5xYfv+3lDhvWj5aUW4dae+x3eteztnko3iKgIzntaW9GR7x+Vnc8X8KTy8qTMIeTUR4JaLZ3PjwlleaK//Y8kouPeFHdkFQ0Zxz/Pv8EF/JhB+UZ9DVDgJhDLMTS6MNFjSWpw2ixUSEEoRjLXOEVJ+pFIcvSf785YRh6ExPefDCoiEmKq7I1789rJ5Ezl+Kh2sEs1YebNJu/5xmGhn6XefaA9+dNHMTf3kv7b7KD/42Zv8/M3wA/nsG/uDh7SQ7bYYcclJtYLWsH7h1wUys1Tx2zSqjBcTbzpK9Y/MDGlVGcUUvzewNs9EibboTIKpEZikHKE+lZsdPenMBo6835eTAYzAWZPG0r7nWEiD0eara1qmB30jNFGhoWsqmfbz/rTLGt9kuefoydCzaXIq7YbCM+95/p2cEtR6RW2am0yfgI7zb93VE/oMDp6fyqxtdMsls4PwXj3ptXe3ezkOZBsgab/BojlN3H7pWSFTYvRj/ML/jSiyGkDLjPGBSSkalKGLCD68qZPuoyc5aAjxlpnj84ZMr/FLhWvGNqQ40ZcJmb+CQoqu4pZLmnnl3SM599MRQgu7vnRYK77RbCpVpijDUrACIoY4TeGe598JVPeM0pEK2VVmfUqCH68jnk9Bx4U7hH/IumTBgd5TniDAU5dN9MuMouBKJSnTxo1i6rgzYlfGtSgYoijlrQRf2XE4MF2YTmDXVaS1QHZVMIGZuMT3Bh4MQvxkC/DVS8+ieeJYXusK+2vOiCmpgX8ebZJzxJtkXN8n8eK2Q2x89wjXfmQ6T77WjfLNj3HFDFd/NVt6XAtMXQKiLuUE0VBR39ItFzeHJsGoNnLOlDP52xs/AngJfc927AtpdRf6iyYvKcwTkIJCKc/M84s39wdCI51xmTnBq/l59/rtbNvfyxOvdoe1G5FAc9BjahxdHwi4OP+A+feHzmxgwpiGIJs7LkcBCIXfahzx/Gd/8diWkHlKHxe9N+/3ZYLjrr5gKlfMnxI4zlMpJwgEyen5kMr2qzD7d2vrgYLEUYaVwAqICHFdo9q7jwUrk3z0ZxSf/vBULpo9IfgSX9h2MBTGqCtw/jLS8GQo+MOrzmP+tMac5vT5uGjWeG65uJln2vcOSjspGwInTqV5J1LSWhOaJPBKSb+6+2i+2m8DxnQ06ygi3cPAHIeD14+h50QfF0xrDGkmZ9SnggSyOFOOPs/i2RM4lXYDod6Xdnnq9b3BOKLmJY2e5LVJxCy+p5sVeU2HHL566Vw69h7nmpbpzJ/WGMqVMKvBAsybNBaAW3+0ITbC5429x0Paj1IqVOhv9cZs+XMdOWbmv0RxXU+TWNPWxdq2rtj+2IV8AYfe6wuZ9aKJi627elj5VEfOb1EL/Vd29vDKzh4eae3i9z6WjSDTUXxxmphScNHsCcyf1pi1GyvF1n29OfWqxH9v/rTGUDLpyqezEVk3LpwV9AWxPogaIBRu6DtBk1Zp1OWZ9Ze44toFgX/gu09sKejsdUSbqYpfZ86HxrDryIniO/rnXf6JeUHI51eWzQ3ZbQsxf1ojnYffTywgdHXKSmCurpNQrN1jKeh2lmZZZrPgHZDz446u2s0qru8cfJ96owrrmrauHAEB0Np5lKvOn4LpwNVmMUEVrVoKXnTU2/uzzm3TtJbJuDSOruf//P6S2CivlhnjQ5Pgc1sPMLlxVJAYF8VsggSeEGgcVceGHYdZMH0co+qdYHHSMmNc0Ao037OogIde6QwEziOtXaz+algD0BVskzzP0XpccYslkVx/YF/a5T7fEQ7evNBzoo+rLpgaqp4LXnXdPUdPsratK+i0l3EVz7TvDflptAU5nVFBTpDO7I7rzFitKKeKCQgReQCv0dAB3dM68r4APwQ+C5wAvqKUavPf+4z/Xgq4Xyn1g0qNM4peofQZEUZJSftRIwtmjmfcqLpgxfGr7YeKTvwZBdMaR7GvSEVQR+COy8/mua0HkpmeFLxz6H2+/D83MnFsg2eeSPBZXus6xm/f+3KO6asQE89sCNlwRwr6d22WaNATMYRt0/rHHVcyu/PIiSDSKRqe+rAxEWpcV3H0RF/WpCJ+S0836yD+i8e2hGo5mU75YgsOnVEcLXOicyXWtHWFjtcLpai/TGtVukaSNi9d+5HpoY56H501nld9bejVhEEA0Y5yORrA0x2JnueL5zaxqLkpSMbr2Hs8Vzjg9fA2S6jo7VFfUdOYBr52+dk8v/VA4BCf0TSa/ce8nJA6R0Ja4mhfa0xnPDMSyouUM82HOioxXx2salBJDeLHwD8BP8nz/jXAuf6/JcD/AJaISAq4G/gU0AVsEpEnlVJvVHCsAYvmNLHi2gX8pZEtmxSXXN8E5C1xn0Mx4aDtniuf7mDFtQt4YdvBouYiF3JWOUnJV700H0MlHOJWeZXE8a9pmgp/unk3GSPXwFzp6ckrWjIbCBVx02U4msY0MH9aNtpJowhrQsq3u//2Jc20REpXPOqXHA8V+iuAju4BcjrN1dc5Qca7uX99ncO4UXW5ZblVdqWso8VWXt/CM+17Q7u9sfd48YFFxihktVKRrOlwSuOooPxKEto6e4Kw7rwasf8Du+qCqax7Yz8uWT9HKDlSeb/BB29fyurlywJ/T3fPyUBYeY7p2YCXZ7Huzf1+9Ff870pHJd73u4sTfZ6homICQin1gojMLbDL9cBPlJdZtUFEJojIdGAusF0ptQNARB7y962ogDBV847u3FC7WkDhPZx9/S4d3cf4yMzxbNrZMyyczOVkKIUDEEQXfWhsA3c+2Z5TolnnGkDW1NTRfYxPnDuZKY2jAuGwpq2Ly86dHOR46ISzUp61dEYxc8JoL3M9Mg5vgs/WmSpEypGgb7Q28zhkM5PXbz0Qeq6u/vBUrpw/hRVPtIdDjcnNfdE9thdMHxeajPtL9LupyLm1X0BTn5LE1U+T5PIo5fWDcARSqWxIdPR2ageyrjjQvudY0Go2GJvvP9Cl0F3lRdEV4udv7OcHP3szyK6vBS2imj6ImcBu43WXvy1u+5J8JxGR5cBygObm+AJtxTDtkYKX0FRuTPW6GPnCEDUuhOLZC51HKJ7xORjGNqRQKE70Vcr7MDA+/9EZjB1Vx8HeU8FqcKC4Ci9y6mC8g/y1rmPc+qMNuErlODxTAo2j6njgV++GymHrvI5837PpDI+ybX9vTtCE/q5vv/Qs7n/p3aKm0bSrAk3UdPpe0zI9CF01z/3R2RPoOdGXe14hlAHuGJrWf//lttCu5X4M0xlF05h6jpzoL7pvvt9UNIAACCbz6O51KcGBIDLsET97PEo0gMDMYsd/RrR2EhWuuoSKruFUbSFRTQERJ/xVge2xKKXuA+4DWLx48YCewQ07DgemGgWJHrhSae8uTb3WCLk2Xyj8Y0s5wnUfmc7h9/s41Z9J7LB1hCDMLik6vK/WGDOqjr/xy1h886HflJzRXSr5TB0ZBff6ZaSDbX4yoBRY/l73kek8/fpez2xjTG6KXGe9fkZWv9JJQ50TCIliwRWn+l2ead/LV5ZlI5nau4+Fooq0yejZjn2MH527cFLKEx6Xz59C78l+OvYeZ8H0cUHNpFKITphF9xc4ejL3txqX13PrJc1sjOQhiMBvzWnirX29oX11GHDceXUfie6jJ3nQiMoKobIBBLpWmNmq1gxy6D3ZH3KA63FXI+chjmoKiC5gtvF6FtANNOTZXjHiaiWVm2IrulF1DqfS2VDJlJEkVCytP8qi5gn8a8e+2B/oBdMa2d1zgvdOhSf2lOPFnI8bVZe36uRw4uFNnRzq9RLiflmgUNpQkM/iU8gS9NTre7MaRJFZ8+K5TWze1RM4mDv2Hg/VbMo7LrJlNByBjTsOh8w6dQ6cO8VbYRfKLD/yvlewT0dtRW38xTRigDMbUrxXZLEx50NelV+9ijw/4rcR8cxkENaa6xwJEuLMGmIXz2li3Ki60H3Smucz7XtDjYrA01h0kMKqPMJB+2qi+Qpr/EZQcdnQzRPHhqIl850jH5UsxVFNAfEk8A3fx7AEOKaU2isiB4FzReQsYA/wReC2Sg2idVcPK55sr9TpE3MqsgI9b2ojkxpHsWD6ON459D4pX4swnWYisHhOE70fpENqcj6/RF1KeOfQ+7FhihlXsX1/L6PqUyWv5GqRjFueBMO6VDZZbShxfTOG8vsrhDLII5zyK5ZmMp4Z44z6lGeiKmBbjDpeXRX2EQgwd+JY3tpfPLN855ETvHPw/bzPTBKFtJhwAJgwpp49xyQIp30z4tS/eE4T50xt9BIEdUtWPLOb9g2ljOTATTt7aOs8Ghzv4GnET7++N3ZBZ07a7TH1lepSwi2LZ+ck1u2JqZprTuQ6BF2X6imU4xKl0g2/Khnmuhq4ApgkIl3A94B6AKXUPcDP8EJct+OFuf5H/720iHwD+De8MNcHlFIdlRrn2rauIU9aS8Jb+3qR/b05qzHtqAZv1f+tay5gw47DvLVva2yNHfB+7JeeO4nmD40p6LswTVG6zEGhH7c5ycRltQ53LpnbxLeuuYCt+3r57uNbglXpUAjQupTwX3xzRtOYrHM8GtkDng+kzvGib57beoBfvLm/qPN25vgz6Doa7rtg9qwI/C5FOGfyWHYcej9kkqrUvTn0fl/otxq9TuuuHiaMaQibSAUe+PVOL7yUsGahI698f3TBiL9p40bxh1d5lWPvXr89KHOv+dDYev7s0+cHk31cufG0q3KqO2sh0n30ZBBcoFR8jksclWj4ZVLJKKZbi7yvgK/nee9neAKk4tTqfGYKgnyk/SbnOqEpKuf0JDGq3gnKIj+8aXdRc5eDVzahY+/xvKvQOgdWXn9h0FoSCGXKjgR+s/soW/f10nOij7/6/IW0dx9j+/7esibhjR9dx7GT6ZztAkEF4LvXbw+t7i+cNZ4p487gjb3H2dNzEvCKQ+4//gFpP/KmmICICgfwtNb6lMPh907lvO/42qo2Zeltv3fpPO58sp1+vynSLRc3s/vIicSl0ktBf9Z8ZJTXklUMJ4Kub5QP5e9TbB7Yd/wUf/l41jxV50jQKlQBPe/3h6o2mxN3JuNy1QVT+eVbB0K9Q4BQkyitBZZiXiql4ddAOO0zqW9cOItHN+/OadJSbgqtyAWYOSG8otPPeLFRHeo9FSQLOf7J9HEK70E21dWV17eEVsMpI7lJn6Oh3gmayevx3bakOShqNmXcGXzNb/1oZhOvbesKEgxrVfCWQn9GBYXTxK+vtedo4UmqVI6dTAcrWPOe9UeSLk3t8LWuYzSkjucI+qnjzuDNfb1B7+xSw4GjeRjgLQQ+ef7UIDT3N7uPBosGV0Hn4feDypIiwoHeU2x490jVzJQuMGfC6JxKA4U03KTjNI9Nu4qrL5jK2/t72Xn4RI5jOdqDY//xDwK/kNliNhAift7ETL9nRVItwOz5PtJ8EDXBojlNrF6+LGhOowtklYoAH/HrFz3+m66QH8ARQnWdtDPPVdkJ+T9deW7QBlPwCn7tP/5B0bLTR0/0BVEnKYFbLvFWcDpjN6quxrV+hGz8vjnhRxO9Fs1pilWhte3TLC3ywK93Jk5iqmWCSVipASccasaPqePYiVxtIaOgISU57S3jki41cQuaD/ozQe/sqHBIOV4Ezetdx0p6vhc2N/HCtoPB9zznQ2NCpqfHX90T/Gb6M4O/R+UgrgxN4xl1vHcq994PBt1FEbLNgfRvx4xeemTz7uC+6zBgvZ+5+jeLEpZCJTOvT3sBAdkbfKNfF+fhTbtLziKuT0lQO/62Jc2hHs160jVbfEbfM5vBKDwHa12qmKHA737l/62rRkI2Y9ds+QgEKw3dycy8B1EKrUzi2liu8XtL/3r7IZrGNsT2P9DMnHAGe2LMHCOZOOGgKYcGW8isk3Icls2bWFKfC/BCvnUIeF+/y1mTzwwJiAN+pFitc/yDgQkHEfjEOZN4cfuhkNCdN2ls0MtDCPeO0NWa9e66JpNORNS92KHwbyyOoW4eZAWEgSko/vaZN9m0qwcM1T/l66muH5uuTTkC3Lx4dt7a8fmuY3JXpNUjEERimERVdy1UhHBjlAdvXxpoRatf6eSR1i5QyrcVZ5vRF3rgCn2GqO1TQbbFowo3x3HEaySvS207wIdnjD/tBEQ1SWe88NdSTD+OwLuHsnkDOrnra5fNo2PvcUbXp0rSGD6UMKmtllAKXtp+KFSjKSWehq+1ZC+iystz0l0ftfZc57cB0M2RTOEApa3+Kx2xFIcVEDEsmtPET7/2sVgtAHIrd5q1dgbKNS3TQxFLQriUtLaBXzl/StYpaGR2Rpuxa0eZNmWZpjPdgxnI6S88UNsnwE/zOMAV8PmPzuTHL+8M7ldx3aj2mDxMihHG+TQckaDvQLR+1zmTx/J7l86jvftYUGPKEfikX5PI5Odv7Ke+zuGmRbNomTGeX7y1PwgbLVbNd7gJB42rvNLtf/X5CwOf1AO/epdmw9yWcVWota0mk1Es8IMKpkTKp5dKpSOW4rACogD5pHu0cmc51D1t29c2fF2PBci5hnld/X7TmIZQdVEgx1GWybiBc9pVKii3PNAHLnp/Vl7fEvhRTJSC+196l5XXtwSC9t7n3yn9JlURAWZMGF3zAuIi39Txw3VvByanlBBojPOnNbLyqY6QqWne5DPpOdFHy4zxPCpdZPByL66cP4X1b+3HdCUpPMeqjlgzv+op40ax//jwMDlFmTCmnqMFBJjgBWToFrd9Rs9t7X/QTY9MFPj+h2MIXvnyqy6YGmoLkJRKRyzFYQXEICi3c+i2Jc2BoIhep9h141TP6Cp/677eUDtDs5NVkgcunznK1LTqHIm1p6ddFSqV/VykeUoSBAr29q0kCmJLTdQao+oc/vaZN8NVYPEWFa27erjn+Xc4/F44hn+d37pWjP11afNPnj81NuEw7hsod7e+wZIkg1ujhUO+cjONo+qCCTqqgU2fcAYzJ4zOKYEybdwoDvSeCvfiUJ5/8bm3DwblvVdt7OSZ9r1c0zI95/cf/c1VMmIpDisgRgCFVE9TmCya05Sj9RTTgszJP84cZdpFHeOHFWfr1glC2vQVZdq4UVw4awLrYhrApAT+6vMXMn9aI2vbukKNZAQ4e/LY2MSuUiaJYryw7RCf/+gMNuw4XHOToSYui95VXgjyw5s6iQss05vM40QktijgcGJ2U/LGWmDk/8RkSf/opXdpnjiWGxfOYqPR9hZgz9EPcvxpgtfFMV/FXh3Yce/z74T6yncefj/oc79gxvjAnGyWlbc9qS0lUYrqGX3ACj1w0clfq9emEDKFk9fG0i8P4QgZFa59/0z7XuZPa2TpvIk5Zakdgbu/tAiA598+6JvFhE/On8KkxlGhEMBFc5pYMGO8pw25ioZ6h6svmMr2g+EaUjMnnMHXrzw3COvt2HMsNsxTZ5pf0zK9aIvV9j3HQrb0cgqgYtQ5xE7wJvmG0r7nWNFjTdKuylvgsJSOhnFMGF3H0ZjkwHJTqnDQ+T+vx0R6ZdxsTkxdyinq7L94blPIbDxxbANPvdadXdRIfEVmsw6a+Wz1+UmxtuWopWQqpXqGJn+l/IYnKiSEosJpxbULQqG7ZhGyl7YdYtPOIzx4+1IvYc9/D7KJhEAQxO9Ajq3WVLkfvmNZ8PeGHYdzfrR7jn7AnU+2s3r5Mr7/hQuDCJNoLSqdaa41qpffOZw32/xk2o3VfoaCwaSVlBreWojdPfkn3jPqHD4oMtBiwiEVUxUAYNwZdQMOVy1EnZ8BrgNN4qoNiGQ76vWnXc6odziZp1qtI/D535rFqo2dIZNuqdn4MZXVhxwrIEYIlVA9C03+5mo+n3DSE+5d694OEvfMtpHt3cdY7a+ilCJwspu9fE1zWVyYn+7jC95Eb5aqBi95yzyH64bfTwlBprkWPrpctqtUTv2eBdPHsf/YB0bdHII6O8OBUXXCqfTgxlroo048s4H9x08N6n7ECYf6lDBzwmiO7ytePLBUXBc6fW1j0ZwmVl7fEixsdEl2M5xcQUg41KeEmxfPDtoMu0px51MdoaCQvrTn0/n+Fy7ky/9zY+w4UpLbJlZXVChHpORAsALCkpekmkmxnI9vXn1erDP8xoWzWGtka8dll5rmsmK+Fp37YTZyqU9JKNoruvh3lRedEhU+OuKqaUwDdz6VDWe+4/KzmTdpbFDDX1Fa3/JqU0g4NI5K0XtqcP09xp1RDyJF6yaVwsym0Xz9inP4+2e3Jj4mqk06/oa4Nb+Lp91u3HGYm/1qrFo73XPU6zENnkY7LhLtNHfiGP7htz8a1MwyzbDRMGP9HEZD2j//0RmcO7Ux0Lq/60cC1qck6D9RrQ5zVkBYClIOzSSfoMm3PZ9QKuZrMRMddSar6btYOm8i9ZGSFlqARIWPjriC3LDiH7+8Eyhv5dJzJnstTctZCLBUBiscHMjpzlYO9h49yfd8Z21SontefYFXT2r7/l5ad/WgFDgpL6hCJ7z2ZRSrNnaypq0r0E51VrQOFT93yplBb2uA5ZedHXq+4rrHOX5iqt7P9E1EI5eSBI4MJaKGvMFv5Vi8eLHavHlztYdhqSCDLTXQuquHNW1dHOo9FXJ+aw1CC598SYN3r9/OPzy7NcfM0uCv9jq6j3Gg9xTrIz2jAb7kFzx8pn0vZ9SnQtFaQtYpOZx+kQJ86sNTuWj2BJ7t2Jfj60g5wqyY4nlDNTbIrsR1RFFdykv0E+IdxSmBP/n0/GCBoIXEI5s934QjXoTRLRfnhqWbzyfk5jDVIiLSqpRaHPee1SAsw4rBajSFkh+TmNP0KjHalvPmxbNDk4XOOfjlWwdQfqSVWfCwdVcPL247GJxHx8hjnFOAurrCzYKqjSPZQIKmMQ281pUtif2pD0/lyvlTWPl0R9Aze+yoyjia9VjM+3THZfOChNNo+W1dNfWRSCXnaNE9MKoSGKXUP71gWt6cpagfbjhjBYTF4pNE+Ji+jkdbu4L6/VEH4qI5Tfzodxfn1Xj0efTKNJpc+JFZ4/n0gmk0jWlgxRO52em1ghjhZ3Gmk7vXbw8mZkcGXjSv6DiAv/78hTy39QD7j38Qu7qPmicXzWni5sWzAy3CkdxiepqkoeRDXUyv0lgTk8UyQMo1GWhtwyx89/0vXMhtS5r5zmNbQv2PKxXqWQqTzmzg8Ht9QYl50xwTxQwtrnTHQX3PimX8m9uTmhbzHR99f6iL6ZUDa2KyWCpAuUKLtbYRV3IhGvv+XgLhYJbMMLclnZvrU16yY8YFx+9C5fq2FaWylXqjvQ3yYnR3qyQPb+pk/rTGvJN03PdVSg5Rse+7GsX0Ko1T7QFYLBaP25Y0839+f0nINHLDwlk0+NVvU44kmuRvW9LM1R+eGtomUUmTh4tmjefmxbOzznIFV50/hbOnnJnThOjCmeOLrpK17X4o7BRTx52RM0mvaevi7vXbg34oUcppEtJmqFRSwTkMsBqExVLDLJrTxOqvLg3Vw4omA5qkHOEGvyGVjpISsh3mBK8MRFvn0dj8jQUzx4fyU1Iph+e2HogtwNgyc3zRSTXOqV8J6lPCHX4b3CDU1JGge2OcyUebhE71u6EeKQOlGsX0Ko0VEBZLjaMnmrVtXVx27mR2HHwvtjAh+E2t8CbmUfXZiVL3FXFEmDCmIcgENxWL+pQEYb96ojMTxULXcUiU2avP9ac/fZWdhysT6vqlJc1BhBhk82i6j55k9SudeU0+G3YcDgSX7pEyf1pjRaLkhqvzuqICQkQ+A/wQSAH3K6V+EHn/z4EvGWO5AJislDoiIjuBXiADpPM5USyWkU60S1lKvE5lmYzCcWDymdk+DLo8ydevPCeYKJvGNLB+6wF++dYBMq7iua0HqEs5pNMujiPcfulZQTho1F5vJoqJwNxJZzJv0tiS+hksmtPE8svO5juPbSm+c4lcMreJv4lpn6vHviYmU1+zdN5EUkaZFFepivgNhqvzGiooIEQkBdwNfAroAjaJyJNKqTf0PkqpvwP+zt//OuCPlVJHjNNcqZTKX1rTYqlRyrli1HZ1TUbBp+ZPYUrjKB7ZvDsQDlGnsb6uNqNoE086o5g3eQzvHj6BqxQ/fnln3kmrXGaT+dMay96HPOXAt665IO/7xcZu1l3SBfUq4TcYzs7rSmoQlwDblVI7AETkIeB64I08+98KrK7geCyWIaHcK0bdR8PkwHFvou3PeA5gh/gYfj05mfZ/F3jn4PvBtmKTlrkiv3v99pIFhWnrLydXnT81Ud5KoX3mT2vkty+ejUDITFVOqtEJrlxUUkDMBHYbr7uAJXE7isgY4DPAN4zNCnhWRBRwr1LqvjzHLgeWAzQ3D9zBZLGUi8GuGKPaR8+JvpwwVd3GUuM4FE3wSjnCBdPHsWXPsVB10qTdBAcq9OKEVCk4AjPGn8Gxk/2helGTBtnjOfqZkvhUBqIZDmfndSUFRFxgXb5n5DrgVxHz0seVUt0iMgX4uYi8pZR6IeeEnuC4D7xEucEO2mIZLINZMUabNK28viVwOGszU1y9JpWnW0B0cgKCxLCUI0H10mKT1mCE3tJ5E/O2ok2Cq7x2pmbUVYPvUB8MpX6mYkKykPAoV87MUFNJAdEFzDZezwLiW1TBF4mYl5RS3f7/D4jIY3gmqxwBYbHUGoNZMZqTlqu8yJqH71gWcjjf+VRHyCcB4LqKtXk6jkUnp4GMTQu9vn4XRHhu6wG6j54MCZdCZUWumD8ltrd1EkTCJdUdgTs/18LWfb3cte7t2F7OpXympIK8kEAZzo7oQlRSQGwCzhWRs4A9eELgtuhOIjIeuBz4HWPbWMBRSvX6f38aWFnBsVosZWWgK8ZoO1bX9VpN6uJyi+Y0hRotaRTwyObdibSBgYxt0ZwmVly7IOgCuGlnD5t29vBIaxerv7oUIHaC1EJjMJw/tTFURtxV8NhvuoKy27q3QqlColRBXkigDGdHdCEqJiCUUmkR+Qbwb3hhrg8opTpE5Gv++/f4u34BeFYpZQZ2TwUeEy/9sw5YpZT610qN1WKpFUKRNa6iLpVN9qrzTUItM8YHOQ6IBG1box34yoWe5F/dfTTUYxyyk+GeoyeDSKm+fjdY2Qcltp14E1gxHIG3YnpMbI70zXimfW9BAVFIu0lyv/TxcV0VYXg7ogthi/VZLDWInpDMZC/wHHuj6rPtX3V2dZJic0muF1fgLhoma9JQ5/B7H5vLj156NyQ8HCHQhHSJ7IY6h1ODaaxdAF2oL47Bmn+SHj9ck+FssT6LZZgRTfYy+0bojne6z0G+VW1SCk2AcRFIApw9eSxL5k1kwYzxQf9m833tQ9EoGLRwyFdwMCVeuGo+ouaftW1dsR0Hkx6fT0sbro7oQthifRZLDaNt/6aFJpVyaBrTwJfu38A/PLuVlU93DGrVGjcBarTpRE8UjngazN/edBF/84UL6TnRFxIOKUdIpQZmTiqGrisVxVUU9HMsnTeRupRX8NBxhIc37+bBjZ2s2tjJrfe9nLeQn3n8SCvClxSrQVgsNU7Pib5Q97qbFs2i50RfMKmf6neDCKaBmDni7OfmeXSDpEO9p5jcOCrkCA9CcPvdoPdye/exUA+LchKnQSjikwnDO2Wd/ma0bX+muN9mOOcxDBYrICyWGkVP0k1jGkIT+I0LZ7F1Xy8iXoMGhdcLYcGM8YFTuBRbe75cCX2eFdcuYG1bVyihLCpA9Dh7TvQxblTutPKpD09l2/7eihTsc/CEaD7MkuPRArYph8Td4U4nwaCxAsJiqUGiCXNmQT2AlU93hEw7adcTEgMNtTQnQLNNaH/a5Zn2vTk9FkyB8eDtS1k6b2IwXok0n/jUh6fytcvP5o7/Gx9AMrNpNN09J+P9C47XrCiVEgRvxQ9esUIHL3KrmNnH1JDEj/rS5qpbLm4eUd3hyo0VEBZLDRJNmPvRS+/y0zuWAXDXurdzEuXAa5izdX/voEMtoyana1qms2nnkeC1QKzPQm8zOws1pIQr50/ht+/9NZk8PupJYxvY03My9r3rPjKdw+/3cU3LdOZPaww5l/V9StIJztRyzKivfOU1zPuvw3bjSpmMdKyAsFhqCF1e+0DvqdD2jKu49/l3eGHbwSCiyYzqaahzuOPys7nj8rMHbSuPs7nPn9YYMkHFldGONgYS4ObFs2nvPpZXONSlhFsububNve2xpTiefLUbF/jVtkNc/eGpOWXGB6IhmZ8l3/Fm5rgL/Gr7ITbtPHLaaRI2D8JiqTKmr8EsoxEN67xo1vig0J4DfPzcSSyYPo6OvccHXG5isGM2J1kt3B7ZvDsw/Tx4+1LWtnXxYB6n9SVzm/jp1z4WhPMe6j3FL7ceIJ2nblNDncPqr5Y316PQ/nete5tfbT+Eq7xw2j/59Hy+fuU5JV+7lrF5EBZLjRL1NUTbgKbEc6zW+yvtrfs7QqYf7ZTetPPIoLuhlUKc01Zv0y1PzYl41SudOT2twfv8qzZ20nOiL8hJ+IvHtrAqUkpEk7SoXpzwKtWnsGhOE9+8+ryQee10CnEFKyAslqpi2rpRipSTLZ1RnxLu/FxLThLcM+17uaZleijUtVSndCWzfqPCY9GcJu74xDzueWFHzr4ZRahhz4O3L+WGhbNY4zvBo1FHxSbpfIKgWLJboVIcp2uIK1gBYbFUlahDeMW1C+joPpaT5du6q4fvPLYlqMu0aecRPrNgGpC8p4Nm1cbOIPt5VP3QROh8+7MX8Is397Mtppe21pr0xG22S+092c+PXtxBxjfx3HndgoJjzScICtVKKqZdnK4hrmAFhMVSVZKsUOPqIZ3qd3n81Wz1/K8sm5soUa51Vw8rnmgPJuW+/sE3NErK5xfO4u/+bWtom+CFsiqlctqlLprTxN3rt4dMTYXyHSB/0bxC93mkVmItB1ZAWCxVoJQkrGg9JNH/MWbOl3ccTqQZbNhxOFQjyXFkwA2NktryTSf8GfXZyCBHCBLx8tWSKrVKaiFBkO8+j9RKrOXACgiLZYgpdZKNtg29efFstu3v5RWj5PWoOieRZhCEbxod6wba0GggXdjMKrRJCgwOxAdQqknodPczFMIKCItliCl1ko2bwFp39fDF+16mP6OoTwnnTm1ks1F0Lp9mMNjJMCqsuo+epHVXT97zRD9rz4m+mgwTPZ39DIWwAsJiGWIGYtKIiwx6aPmynOS1JJrBYCZDLWDWtHXxaGsXq1/pZE1bV14tSFdS7U+7pFKlm29syYvqYgWExTLElMukEZ3oh8pMosNG05mEWpD2eRRIys3n+E6ibQ3XRj3DASsgLJYqUAmTxlCaSZJqQWYl1f6MCsqSmxTSEopdx2oYlcUKCIvFUjJJtSBtYtJRWA9v3p2T41FISyh2naT+HKtlDAwrICwWy4AopLGYE/JNi2ax2i+dkc4oVm/s9Ooz+av9YlpCoesk0WSsljFwrICwWCxlJS60dVS9k9NXW5cJH0xf7SSaTL6WqlajKE5FBYSIfAb4IZAC7ldK/SDy/hXAE8C7/qa1SqmVSY61WCy1SVxoq67qalZ61X21B7uyL+Z7iWoZ5bru6UDFBISIpIC7gU8BXcAmEXlSKfVGZNcXlVLXDvBYi8VSY8SZfeIqva5p6wq0ikqWuIhqGba0RnIqqUFcAmxXSu0AEJGHgOuBJJP8YI61WCxVJEm5i9ZdPTza2hVUC0mVWPJjIGMyx2FLaySjkgJiJrDbeN0FLInZb5mIvAZ0A3+mlOoo4VhEZDmwHKC5eegaplgslvwkqS+VzmQbI928ePaQreJtaY3kVFJASMy2aKZMGzBHKfWeiHwWeBw4N+Gx3kal7gPuA6+j3IBHa7FYhoyoGSpfb+hKYUtrJKOSAqILmG28noWnJQQopY4bf/9MRP5ZRCYlOdZisQxf7Cp+eFBJAbEJOFdEzgL2AF8EbjN3EJFpwH6llBKRS/Ba7R4GjhY71mKxDG/sKr72qZiAUEqlReQbwL/hhao+oJTqEJGv+e/fA9wE/IGIpIGTwBeVUgqIPbZSY7VYLBZLLqIKFNAabixevFht3ry52sOwWCyWYYOItCqlFse95wz1YCwWi8UyPLACwmKxWCyxWAFhsVgsllisgLBYLBZLLCPKSS0iB4FdFbzEJOBQBc8/nLD3Ioy9H1nsvQhT6/djjlJqctwbI0pAVBoR2ZzP23+6Ye9FGHs/sth7EWY43w9rYrJYLBZLLFZAWCwWiyUWKyBK475qD6CGsPcijL0fWey9CDNs74f1QVgsFoslFqtBWCwWiyUWKyAsFovFEosVEDGIyGdEZKuIbBeRb8e8/yURed3/92sRuaga4xwKEtyL6/378KqIbBaRS6sxzqGg2L0w9rtYRDIictNQjm+oSfBsXCEix/xn41URWVGNcQ4FSZ4N/368KiIdIvL8UI9xQCil7D/jH1558XeAeUAD8Brw4cg+HwOa/L+vATZWe9xVvBdnkvVlfQR4q9rjrta9MPb7JfAz4KZqj7vKz8YVwNPVHmuN3IsJwBtAs/96SrXHneSf1SByuQTYrpTaoZTqAx4Crjd3UEr9WinV47/cgNfxbiSS5F68p/wnHhhLntawI4Ci98Ln/wHWAAeGcnBVIOn9OB1Ici9uA9YqpToBlFLD4vmwAiKXmcBu43WXvy0fvw88U9ERVY9E90JEviAibwH/AvzeEI1tqCl6L0RkJvAF4J4hHFe1SPo7WSYir4nIMyKyYGiGNuQkuRfnAU0i8pyItIrI7w7Z6AZBJVuODlckZlvsqlhErsQTECPV7p7oXiilHgMeE5HLgL8Crq70wKpAkntxF/AtpVRGJG73EUWS+9GGV+fnPRH5LPA4cG6lB1YFktyLOmARcBUwGnhZRDYopd6u9OAGgxUQuXQBs43Xs4Du6E4i8hHgfuAapdThIRrbUJPoXmiUUi+IyNkiMkkpVcvFyQZCknuxGHjIFw6TgM+KSFop9fiQjHBoKXo/lFLHjb9/JiL/fBo/G13AIaXU+8D7IvICcBFQ0wKi6k6QWvuHJzR3AGeRdTgtiOzTDGwHPlbt8dbAvTiHrJN6IbBHvx5J/5Lci8j+P2ZkO6mTPBvTjGfjEqDzdH02gAuAX/j7jgHagZZqj73YP6tBRFBKpUXkG8C/4UUnPKCU6hCRr/nv3wOsACYC/+yvFtNqmFZrLETCe3Ej8Lsi0g+cBG5R/i9iJJHwXpw2JLwfNwF/ICJpvGfji6frs6GUelNE/hV4HXCB+5VS7dUbdTJsqQ2LxWKxxGKjmCwWi8USixUQFovFYonFCgiLxWKxxGIFhMVisVhisQLCYrFYLLFYAWGpGURkrojUROifX3nzaf/vzxWp3vpRP1M43/uLReQfi1zvOwMfbeg8c0XktgLvnysiT4vIO37Jh/V+Brx+//N+dd63RGSLiHzeeO/HIvKuX5G0TUSWlWPMltrFCgiLpQhKqSeVUj8osMtHgVgBISJ1SqnNSqk/LHKZsggIYC5eYbi4sZyBVy/rPqXU2UqpRXjFBef5718E/D1wvVLqfOBzwN/7VQM0f66U+ijwbeDeMo3ZUqNYAWGpNVIi8iO/Zv6zIjIaglX6Bn91+5iINPnbnxOR/yYiL4jIm34vhrUisk1E/lqfVER+R0Re8Ve/94pIKnphv6b/WyLyEnCDsf0rIvJP/t83i0i7X4DuBRFpAFYCt/jnvkVE7hSR+0TkWeAnEW3kTBH5X/7q/HURuVFEfgCM9o9/MGZc74nIP/ir9l+IyGR/+zkiss4fS5uInA38APiEf64/jpzqS8DLSqkn9QalVLtS6sf+yz8Dvq+Uetd/713gvwJ/HvM9vYCXRW8ZwVgBYak1zgXuVkotAI7iZWoD/ASvEN5HgC3A94xj+pRSl+FVUX0C+DrQAnxFRCaKyAXALcDH/dVvBm+yDPBX1z8CrgM+gVcmIo4VwL9TSl0EfE555Z1XAA8rpT6qlHrY328R3ko8upr/LnBMKXWh/1l+qZT6NnDSP/5L5DIWaFNKLQSeNz77g/69ugivR8levJX9i/65/lvkPAvwCujlYwHQGtm22d8e5Tq878EygrECwlJrvKuUetX/uxWYKyLjgQlKKd2F638DlxnH6BXxFqBDKbVXKXUKrz7ObLwKmouATSLyqv96XuS65/vX3uaXg/i/ecb3K+DHIvJVvLIK+XhSKXUyZvvVwN36hcr2FSmEC2jB83+BS0WkEZipvEq6KKU+UEqdSHCuAF8TaxeRtXoTuVVIo9v+zr+Hy/EqGVtGMLYWk6XWOGX8ncErjZz0GDdyvIv3jAvwv5VS/7nIeYrWnVFKfU1ElgD/HnhVRD6aZ9f382yPm4RLRRFfYroYHRiCVSn1BRFZjOd30O8vxqsXpFmI1wlN8+dKqUcHcG3LMMRqEJaaRyl1DOgRkU/4m76MZ2pJyi+Am0RkCoCIfEhE5kT2eQs4y7fjA9wadyIROVsptVEptQI4hKeh9AKNCcfyLPAN43xN/p/9IlKf5xgHr/AdeA7ol5RXSrtLRxmJyCgRGVNkLKuAj4vI54xtY4y//x74zyIy1z/nXDzn+T8k+mSWEYcVEJbhwn/AM2+8jhc1tDLpgUqpN4C/BJ71j/85MD2yzwd4ZpN/8Z3Uu/Kc7u98B3M7nqP2NWA98GHtpC4ynL/G6yzWLiKvAVf62+8DXo9zUuNpIwtEpBX4JNnP/mXgD/3P9Gs8v8nrQNp3XIec1L7J61rgayKyQ0Re9u/LX/vvvwp8C3hKvA6BTwH/r2Hys5xm2GquFkuNIyLvKaXOrPY4LKcfVoOwWCwWSyxWg7BYLBZLLFaDsFgsFkssVkBYLBaLJRYrICwWi8USixUQFovFYonFCgiLxWKxxPL/A7R4H5NY0AR4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is the correlation of HD area to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )   #need to calculate HD area before do\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()\n",
    "print(\"... and here is the correlation of tract usage to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "e131623f-1b84-4a2a-857d-f46d99aa9c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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u5bjHiblzB41yDQt4uCaQUxCjdwTuQaPe+vWDESPg6FHoHmAypcGDreXgQbj2Wt99pxt8mpmZSfXq1T3rSUlJrFq1KuCxH3/8MY888gj79+/ns88+A2DHjh1UqlSJW265hfXr19OyZUteeeWV0yaHS09PJy0tjVKlSlG/fn3uuusunE4nDz30EGvXruXcc8/lyiuvJDU1lauvvtrzut9++42PP/6YzZs3IyIcOnTIs+/3339n8eLFzJ07l549e7Js2TLeeustWrduTXp6OklJSTzzzDMsXLiQs846i+eff56XXnqJJ554gjvvvNOTQuPGG29k3rx5noyphw4d4uuvv87/B6kKb9kruaqEJmX3yNUwDFYPIaVA7wiKRKA0HeLdYdtLnz592Lx5M6mpqTxuN1RmZ2ezbt06hg8fTlpaGmeddVae7QzeOnfuTPny5SldujSNGjVi9+7dfPfdd3Ts2JFKlSqRkJDAwIEDWbp0qc/rzjnnHEqXLs1tt93GnDlzKFu2rGdfz549ERGSk5O54IILSE5OxuFw0LhxY3bt2sXKlSv54YcfaN++PSkpKbzzzjvs3r0bgK+++oq2bduSnJzM4sWL2bRpk+e8/fv3P/0PMpwqVrSWGHN423Lw+nP8X075XLmDQHsIKV8xeUeQ3zf4smXz35+YWPD0M0lJST4ZQzMyMqhatWq+r7n00kv56aefOHjwIElJSSQlJdG2bVsArr322qACgX/q6+zs7IBByV9CQgKrV69m0aJFzJgxg4kTJ7J48WKfc3qnvXavZ2dn43Q6ueKKK/jggw98zpmVlcWIESNYs2YN1atX56mnniIrK8uzv9ilvrYnDoolaR+/TEr2bz7b1pu6uY7TxmHlT+8IikDr1q3Ztm0bO3fu5MSJE8yYMYNevXrlOm779u2eD+p169Zx4sQJKlasSOXKlalevTpbtmwBYNGiRbkamoPVtm1bvv76aw4ePIjL5eKDDz7Ilfr6yJEj/PHHH3Tv3p0JEyYEnM8gL+3atWPZsmVs374dgKNHj7J161bPh35iYiJHjhxh1qxZZ1R+debOTnsTODVmwACTXT18jnGANg6rXEJ6RyAiXYFXsKbCfssYM9Zvf3lgOlDDLst4Y8yUUJYpFBISEpg4cSJdunTB5XIxZMgQGjduDFiTzgAMGzaM2bNnM23aNEqUKEGZMmX48MMPPVVIr776KgMHDuTEiRPUrl2bKVOsH8PHH3/MXXfdxYEDB7jqqqtISUnhyy+/zLMsVapUYcyYMXTq1AljDN27d6d3794+x/z555/07t2brKwsjDG8/PLLQV9rpUqVmDp1Ktdff71nNrRnnnmGevXqcfvtt5OcnEzNmjVp3bp18D/ASHjETq0wZkxky1FEvl70Ge0l02fbaleDXG0DL2m7gAogZGmoRcQJbAWuADKA74DrjTE/eB3zKFDeGPOQiFQCtgCVjTEn8jqvpqGOPZqGuvBWPt6Wto7NnruBHOC6E0/5BAIdNBbf8ktDHcqqoTbAdmPMDvuDfQbQ2+8YA5QT62vx2cBvQHYIy6RUzLn1XxNp7fCdJnN7TlWfIKDtAio/oQwE1QDvORcz7G3eJgINgZ+BjcA9xpgc/xOJyFARWSMiaw4cOBCq8ioVdUanbqTT8cU48M0nNMXVzXOMtguo0wllIAjUf9K/HqoLkA5UBVKAiSJyTq4XGTPZGNPKGNOqUqVKRV1OpaLW9JV7SOQPn22bXBf6TDKj7QLqdEIZCDKA6l7rSVjf/L3dAswxlu3ATqBBCMuklCUpyVqi2MA3rXxUFTjisz2DU1+W2l90no4XUKcVyl5D3wF1RaQWkAkMAPxHtuwBOgPfiMgFQH1gRwjLpJRl+vRIl6BQUtMyWfbTb7SQrbRybvHZd5DygOYRUsELWSAwxmSLyJ3Al1jdR982xmwSkWH2/knAv4CpIrIRqyrpIWPMwVCVSalYcd9H6QD0dX6DE+NpH3AhzHF1ADSPkApeSAeUGWPmG2PqGWMuMsY8a2+bZAcBjDE/G2OuNMYkG2OaGGOi+mva6VJR55WGOisrizZt2tCsWTMaN27Mk08+meu148ePR0Q4eDB64uTgwYOL78CykSOtJQq1fXYBOXZrWx18xw6scdVnnanHoHY1IlAyFa1iMsVEJASTijqvNNSlSpVi8eLFnH322Zw8eZJLLrmEbt260a5dOwD27t3LggULqFEj8v+5XS4XTqcz0sUovAKMpi5ORqdu5Jc/rWE2AxyLaOP06zZKNRygXUVVgWiKiSISTCrqvNJQiwhnn302ACdPnuTkyZM+Sevuvfdexo0b57Nt06ZNtGnThpSUFJo2bcq2bdvYtWsXDRo04LbbbqNJkyYMHDiQhQsX0r59e+rWrcvq1as97z1kyBBat25N8+bNPeXctWsXHTp0oEWLFrRo0YLly5cDsGTJEjp16sQNN9xAcnIyLpeLUaNG0bp1a5o2bcobb7wBWMn37rzzTho1asRVV13F/v37Q/GjjmvTV+4BoIVs5dkSbyPgM4hsjquD9hJSBRabdwQLO+beVqMf1BsB2UdhSYA81LUHW0vWQfjWLw/15UtO+5bBpqIOlIYarG/aLVu2ZPv27dxxxx2eBHRz586lWrVqNGvWzOc8kyZN4p577vGkpXC5XPzyyy9s376dmTNnMnnyZFq3bs3777/Pt99+y9y5c3nuuedITU3l2Wef5bLLLuPtt9/m0KFDtGnThssvv5zzzz+fBQsWULp0abZt28b111/vmRVt9erVfP/999SqVYvJkydTvnx5vvvuO44fP0779u258sorSUtLY8uWLWzcuJFffvmFRo0aMWTIkNP+7FRw3L2EAIY65+Gw2wbctudUpUztdtpLSBVYbAaCCAg2FXWfPn3o06cPS5cu5fHHH2fhwoWAlT00PT2dQ4cO0adPH77//ntq167Ns88+y3//+99c57n44ot59tlnycjIoG/fvtSta2WZrFWrFsnJVrVA48aN6dy5syet9K5duwBrYpm5c+cyfrw1Y1VWVhZ79uyhatWq3HnnnaSnp+N0Otm6davn/dq0aUOtWrU8r9+wYYOn/v+PP/5g27ZtLF26lOuvvx6n00nVqlW57LLLzvTHqfy4ewmBdTdwhfNUmhX3n97sEj21l5A6I7EZCPL7Bp9QNv/9pRODugPwV9BU1N5pqBMTEz3bK1SoQMeOHfniiy/o0qULO3fu9NwNZGRk0KJFC1avXs0NN9xA27Zt+eyzz+jSpQtvvfUWtWvXzpU62jutdHa2lb3DGMPs2bNzzXz21FNPccEFF7B+/XpycnIoXbq0Z593GmljDK+++ipdunTxef38+fPznIeh2KmXe6KW4uyBmes9z19IeN0zkthtlasBj/5zXPgLpmKCthEUkWBSUeeVhvrAgQOeWcKOHTvGwoULadCgAcnJyezfv59du3axa9cukpKSWLduHZUrV2bHjh3Url2bu+++m169erFhw4agy9qlSxdeffVVT1nS0tIA65t9lSpVcDgcvPvuu7hcrjxf//rrr3Py5EkAtm7dyl9//cWll17KjBkzcLlc7Nu3j6+++qpAP8OwmjzZWqLA6NSNZNvdhF5M+De1Hb949rnTTa9vODIyhVMxITbvCCIgr1TUwaSh3rdvHzfffDMul4ucnBz69etHjx498n2/Dz/8kOnTp1OiRAkqV67ME088weHDh4Mq6+OPP87IkSNp2rQpxhhq1qzJvHnzGDFiBNdccw0zZ86kU6dOeU4mc9ttt7Fr1y5atGiBMYZKlSqRmppKnz59WLx4McnJydSrVy/XPAjqzLgbiB90vk9f5zLAN69Qqqs9/xh4faSKp2JAyNJQh4qmoY49Efn9DR1qPRbhXUEo0lAPfHOFZwTxrJJPeXoJgRUIdrgqs/GaxdpArE4rvzTUekeg4pNXQ3hxNTp1o6eB+IWE13MFgRxgWuUH+acGAVVIGgiUKoZS0zI9VUJ5tQs8dvJWxt55a4RKqGJJzDQWR1sVl7Lo7y0wdy+hvNoFPna1J6HNLZEqnooxMXFHULp0aX799VcqVqwYPd0XFcYYfv31V59uqupUL6EWspVhCfMA366iO1yVGZV9Bzs0jYQqIjERCJKSksjIyEBnL4s+pUuXJikS8wKkpIT/PYPkrhJ6OmFKwHaBUa5hmkZCFamYCAQlSpTwjHpVKigTJkS6BAG500gMcCyisWO3Z7t3u4CmkVBFLSYCgVKxwDuNxMiE2YBvu8Ck7B6sTezFAk0joYqYBgIVnwYNsh6L0Uxl3g3EF8ghn33/yynPONcN7LqvY/gLpmKeBgIVnzIyIl0CH+4G4gGORT4NxO67gVdc1+pkMypkNBAoVQxMX7kn1xwDbptcF/JRTmftJaRC5rTjCESkr4hsE5E/ROSwiPwpIsEltVFKnZa7gbiv8xufOQbcDcRPuG7RXkIqpIK5IxgH9DTG/BjqwigVb7zTSHSUdZ7t3g3E2ktIhVowgeAXDQIq5lwc+Z433mkkpiSMoZrjd5/9e3ISrQZi7SWkQiyYQLBGRD4EUoHj7o3GmDmhKpRSITdmTKRL4Okl9GLCv+no3Aj4NhC/7uqtDcQqLIIJBOcAR4ErvbYZQAOBUmfI3Usor1xCS1zJ2kCswua0gcAYo5mtVOy55hrrcfbsiLy9u5dQXrmEbsl+hAnaQKzCJJheQ0ki8rGI7BeRX0RktohEIDmMUkXo11+tJQLcvYTymmNglGsY7S86TxuIVdgEk4Z6CjAXqApUAz61tymlCsidRiK/OQb+rNSc97SBWIVRMIGgkjFmijEm216mApVCXC6lYtIDM9fnO8fAjJzOLNA0EirMggkEB0VkkIg47WUQEJl7aqWi2OjUjVzLwjzbBe7PvkN7CamICKbX0BBgIvAy1t3rcnubUtGrc+ewv2X26ik8V+I/ebYLOIBntJeQioBgAsF+Y0yvkJdEqXB6/PGwvt3d4ybxsl8eIe92gXWmnvYSUhETTNXQ9yKyTETGikh3ESkf7MlFpKuIbBGR7SLycB7HdBSRdBHZJCJfB11ypaLEwDdX0P3wzFx5hOBUu4D2ElKRFMw4gjoiUgPoAPQAXhORQ8aYlPxeJyJO4N/AFUAG8J2IzDXG/OB1TAXgNaCrMWaPiJx/xleiVEF062Y9fv55SN8mNS2TYztWckXJNZ5t3oPG7s++A0B7CamIOm0gsMcMtMcKBM2ATcC3QZy7DbDdGLPDPs8MoDfwg9cxNwBzjDF7AIwx+wtUeqXO1LFjYXmbB2auJzVhCg58G4dXuRpwS/YjANpArCIumDaCPcB3wHPGmGEFOHc1YK/XegbQ1u+YekAJEVkClANeMcZM8z+RiAwFhgLUqKH/aVR0cPcSCjT38DjXAABtIFbFQjCBoDlwCXCDXc+/DfjaGPOf07xOAmwzAd6/JdAZKAOsEJGVxpitPi8yZjIwGaBVq1b+51CqWJq+cg8rSwaee3idqQeg8wyoYiGYNoL1IvIT8BNW9dAg4FLgdIEgA6jutZ4E/BzgmIPGmL+Av0RkKVb101aUimID31yR79zDYFUJaQOxKg6CaSNYA5TCGj/wLXCpMWZ3/q8CrOqkuiJSC8gEBmC1CXj7BJgoIglASayqo5eDL75SZ6hHj5CdOjUtk+o7ZzKsROC5hwHaX3SeVgmpYiOYqqFuxpgDBT2xMSZbRO4EvgScwNvGmE0iMszeP8kY86OIfAFswBpX85Yx5vuCvpdSBfbAAyE79XuzZjLDb+AYWHMPz8ixBrJpLyFVnARTNVTgIOD12vnAfL9tk/zWXwBeONP3UKo4+XHfYZ6s+bZPLyHvuYdBewmp4ieYAWVKxZ6OHa2lCB08cpxSWQdy9RKCUw3E2ktIFUcaCJQqIjsO/EWSWDfQ3u0Cc1ztPQ3E2ktIFUfBTExznYiUs5+PFpE5ItIi9EVTKnqkpmVSjV8oQbbP9v/llPeMHtY0Eqq4CuaO4HFjzJ8icgnQBXgHeD20xVIqurw3ayZVxcrOHqiXkANtIFbFVzCBwGU/XgW8boz5BKurp1IKa8zArfIp4DuK0ruXkFYJqeIsmO6jmSLyBnA58LyIlELbFlS069evSE7jHjNwZYk17KEicGqOAXcvoRIOtEpIFWvBBIJ+QFdgvDHmkIhUAUaFtlhKhdiIEUVymg9mz+J995gBr+0LXK08aSReuC6lSN5LqVAJ5pv9G8aYOcaYbQDGmH3AjaEtllIhdvSotRRCalomtzDXd8yAvUx2WSOXtYFYRYNgAkFj7xV7noGWoSmOUmHSvbu1FMJ7s2ZyhdNrngH78WNXe8+YAW0gVtEgz0AgIo+IyJ9AUxE5bC9/AvuxcgQpFbdGp27kfscHueYZOGpKe7qLagOxihZ5BgJjzBhjTDngBWPMOfZSzhhT0RjzSBjLqFSx88OqhbR2bPasu7uL7qQyoA3EKroEk2voERE5F6gLlPbavjSUBVOquBr45gqGOud57gbcQeBnU5EjpgygDcQqugSThvo24B6s+QTSgXbACuCykJZMqWLIPQfx5V5zEIM1ZmCvsabc1gZiFW2C6T56D9AaWGmM6SQiDYB/hrZYseX51c+z+bfNpz9QhU37llYqiGVf3FKg163e+RsX1tjH7XK+z/ZfTGluKb0PgJLVJ3PLF5OLpqCqWGpwXgMeavNQpItRZIIJBFnGmCwRQURKGWM2i0j9kJdMqRBadkXdAr/m4JHj5BjDOY6/fLYb4CDlgV8pU8JZNAVUKoyCCQQZIlIBSAUWiMjv5J5yUuUjlr45xIyDB63HxMSgX1Ln0fncJ+8xPCEDONU+sDWnKl1OjsQcf43kqhXp0XVKKEqsVMgE01jcx376lIh8BZQHvghpqZQKtWutZHAsWRLU4aNTN9LUbOEfAaafnOLqBsBFlc4u6lIqFRbB3BFgZx6ta4yZIiKVgGrAzpCWTKliZPrKPcwoMSPXuAF3YrkSDkg8u1TEyqdUYQQzH8GTwEOAe+xACWB6KAulVHEyOnUjAxyLaOs3bsA7sZx2F1XRLJgUE32AXsBfAMaYn4FyoSyUUsXJ9JV7GOK0akO97wbcieW0u6iKdsEEghPGGHcuLUTkrNAWSaniw303UMeR6dnmvhuY7Oqh+YRUTAimjeAjez6CCiJyOzAEeDO0xVIqxIYPD+qwH1YtZGZJO810gLuBCZpPSMWAYHoNjReRK4DDQH3gCWPMgpCXTKlQ6t//tIcMfHMFDyb4NhAb45tmWquEVCwIJsXEWcBiY8wCeyBZfREpYYw5GfriKRUie/daj9WrB9ztnnmsTYncieUmZfdgnanHoHY1Ql1KpcIimKqhpUAHO/HcQmAN0B8YGMqCKRVSN9pzK+UxjsCaeextT5WQOwjMcbVnnOsGHMAzVyeHo6RKhVwwjcVijDkK9AVetQeYNQptsZSKnNS0THryNQ6MT7vAnpxEnWtAxaSgAoGIXIx1B/CZvS2ogWhKRaOHZm8gRbZ71t13A6+7egM614CKPcEEgnuwBpN9bIzZJCK1ga9CWyylIiM1LZM+OQto7Njts909ghh08JiKPcH0GlqK1U7gXt8B3B3KQikVKY99vJHPE+YCvt1F06kD6FwDKjZpFY+KT/ffn2tTalomT+f8H9WdBzzb3IPH5rg6ADp4TMUmDQQqPvXsmWvTL7MfYqhzGXCqp5ABHjt5K+tMPcqUCKYmVanoE0zSueATtud+bVcR2SIi20Xk4XyOay0iLhG59kzfS6kC2bLFWmxfL/qM252fAr5VQqtdDTxtA2P6Ng1rEZUKlzwDgYj0FJEDwEYRyRCRvxXkxCLiBP4NdMPqbnq9iOTqdmof9zzwZYFKrlRh/OMf1mKrvuRenzQS7ruBca4BAJQp4dC2ARWz8rsjeBboYIypAlwDjCngudsA240xO4wxJ4AZQO8Ax90FzAb2F/D8ShWJLdPvo5bjF8+6/whi0LsBFdvyCwTZxpjNAMaYVRQ89XQ1YK/Xeoa9zUNEqmGluZ6U34lEZKiIrBGRNQcOHMjvUKUK7IKtM4DAI4hBewqp2JdfY/H5InJfXuvGmJdOc24JsM34rU8AHjLGuEQCHe55r8nAZIBWrVr5n0OpM7b7zYHUEGsyencQWOJK9owgBu0ppGJffoHgTXzvAvzXTycD8M7olUTuSe9bATPsIJAIdBeRbGNMagHeR6kzc/xPqmf4zkF8yJTlluxHPIdoTyEVD/IMBMaYfxby3N8BdUWkFpAJDABu8HuPWu7nIjIVmKdBQIXF6NEc+ngU5fGtEvrAdZnPYdo2oOJBvl93RKSbiCwVkYMickBEvhaR7sGc2BiTDdyJ1RvoR+AjO0XFMBEZVviiK1UI9c/hnMQdPpt+zTnL0y4A2lNIxY887wjs2cj+ATyIlXoarKqcsSKSZNfb58sYMx+Y77ctYMOwMWZwkGVWqtAOTxhCuSMupIrTs22NaehzjN4NqHiRXxvBvcAlxpjfvLYtFpFuwLfYjbdKRZ0FT1Jutj3hzOCzcs06BtpTSMWX/AKB+AUBAIwxv+bXw0flYWHH3Ntq9IN6IyD7KCwJUONWe7C1ZB2EbwMMuq47HC7sD3/thRU35t7f4H5I6gmHt8Dqf+Te32Q0VL4cfk+HtSNz72/2HFT6GxxYDusfzb2/5QQ4NwX+txC+fyb3/jZvwDn1IeNT2Pxi7v0XvwtnVYfdH8K213Pvv2QWlE6EHVOtxV/H+ZBQFra+Bns+yr3/8iXW44/jIdNqFOb4n7BvPZRyIcedVttAxePsK1OeB83bnpe2a1APa3gLkP4IHFzhe+6ySfC36dbztSPh93QuaGMHl4UdoVw9aGt/V1o1FP7c6vv6c1Osnx/A8kFwNMN3f+LFkGIP3fnmGjj+q+/+CzpD8uPW86+6geuY7/5qPaDhA6fK40//9gr3t+f+24oR+bURHBaRZv4b7W1/hq5ISoXQ7zsx+PZt3p9Tnr3mfM/6BeeUDnuxlIokMSZwt3wRuQR4D5gCrMW6e24N3AwMMsZ8G65CemvVqpVZs2bN6Q9Uyt/e1fCfKzAG5J2/MIC5+SyuPfGUZwQxwK6xV53R6XffeBMAF747rShKq1SREpG1xphWgfbleUdgf9C3tY8ZDAyxn7eLVBBQqlCWvWLdDfgllfMOAjpuQMWjfNNQG2P+BzwRprIoFTp7V5OzeR5i1wuZy0qRw6mkcm7aU0jFo/y6j/YGkowx/7bXVwGV7N0PGWNmhqF8MaNjx9zb+vWDESPg6FHoHqC9bvBgazl4EK4N0F43fDj07w9798KNAdrr7r/fSru/ZYtPok2P0aPh8sshPR1Gjsy9/7nn4G9/g+XL4dEA7XUTJkBKCixcCM8EaK974w2oXx8+/RReDNBe9+67UL06fPghvB6gvW7WLEhMhKlTrcXf/PlQtiy89hp8FKCteMkS63H8eJj3VmnImufZVyYhi3sGjGVddj0OLatD1u5ERGDCyopMACpWhNl2W/Ejj8AKv7bipCSYbrcVjxxp/QyzfrRGJJfuCPXqwWS7rXjoUNjq11ackmL9/AAGDYIMv7biiy+GMXZb8TXXwK9+bcWdO8Pjdltxt25wzK+tuEcPeMBuK9a/vdz7C/u35/7bihX53Qc/CMz1Wi+F1UbQEdABYSp6HNoDWYd9NjXI2sTXuxv7bLuo0tnhLJVSxUZ+jcXfGWNae61PNMbcaT9faYxpF6Yy+tDGYlVg37xIzqKncXAqlcTPU0uz15zPgBvGAlDCAdueO7NGYjdtLFbF2Rk1FgPneq+4g4CtEkpFi5odOGkScBnIQXg9u4dPd1GAF65LiUzZlCoG8gsEq+w0Ez5E5B/A6tAVSami9fVW9xwWQjZOFubk/lKko4hVPDtdiolUEbkBWGdva4nVVnB1iMulVJFZs2QulzhcOMVgjIt2jh999g9qVyNCJVOqeMgvDfV+4G8ichngblX7zBizOCwlU6oIpKZlUibnCA6HwRhwYvjN+DYKP3N1coRKp1TxkO84AgD7g18//FVU+mD2LN5LsBLgikC2gfPkCE93Hgro3YBSEEQgUCpapaZl0pOvcZLjNfmMg5U5DfnhgtoAzNe7AaXyn5hGqWj22Mcbc21b6GrBOlOP9rvSebKM/8ypSsUnDQQqZv11wkUZsoBT4wd2mMoA3LV8Brd89V6kiqZUsaKBQMWk0akbaSFbudq5HDiVaK6xYzegqaaV8qaBQMWk6Sv30Nf5DQ6Mz+T0n7vaAFAr8awIlk6p4kUDgYo5o1OttoE6ZPps3+S6kBk5nTXVtFJ+9H+EijnTV+6hhWyllXOLz/Z06gCaalopf9p9VMUU991AX+c3OL2qhVwIc1wdKOGw00m88UaES6pU8aGBQMWU91buAXJXC61x1WedqceEfinWhvr1w1wypYovrRpSMSM1LRMDAauFtlPt1N0AWDOWfPpp+AupVDGkdwQqZrgHkOVVLfSC+24ATk1b1bNn+AuqVDGjdwQqJqSmZfLXCVfAfQtdLVln6mmqaaXyoIFAxQTvdBLf59QETo0dWJLTjAplSkSgVEpFBw0EKur53w00cezCYI0mdmFlG32qV+M8X69UvNNAoKKe991AC9lKf+dXCNjzD8ARRzmtFlIqH9pYrKKa/91AX+c3JNhppwFyDPRvcnbuF777bphKqFTxF9I7AhHpKiJbRGS7iDwcYP9AEdlgL8tFpFkoy6Nij3+q6RTZ7nluDBiExn+7KvcLq1e3FqVU6AKBiDiBfwPdgEbA9SLSyO+wncDfjTFNgX8Bk0NVHhWbvO8GBjgWebKLuv2vSieo3ib3Cz/80FqUUiGtGmoDbDfG7AAQkRlAb+AH9wHGmOVex68EkkJYHhVjBr65wmd9RMJcAM/4AQNUu+qRwC9+/XXrsX//EJZQqegQyqqhasBer/UMe1tebgU+D7RDRIaKyBoRWXPgwIEiLKKKVqlpmSz76TfP+gDHIqqL79/GrxVbBr4bUEr5CGUgkADbTMADRTphBYKHAu03xkw2xrQyxrSqVKlSERZRRSv/toFuztXAqbuBHKBSn7ERKJlS0SeUgSAD8G6NSwJyTRIrIk2Bt4DexphfQ1geFSMCjSI+aM4BTg0iy0jqoXcDSgUplIHgO6CuiNQSkZLAAGCu9wEiUgOYA9xojNkawrKoGPLQ7A0+6y1kK729pqTMAS5s0CICJVMqOoWssdgYky0idwJfYo3redsYs0lEhtn7JwFPABWB18Tq+J1tjGkVqjKp6Jealsnx7ByfbQ8mzPBJMifigJod8j/RrFkhLKVS0SWkA8qMMfOB+X7bJnk9vw24LZRlULEl0N1Aa8dmn22O8kmnrxZKTCzqoikVtTTFhIoage4G2jl+RMAzkhgBqiSf/mRTp1qLUkoDgYoe/ncDABdJhievkAEEB7QfefqTaSBQykNzDamoEOhuoIVs5WrnMsDrjqBac+0tpFQB6R2BigoPzFyfa9sLCa/j4NTYAQCa3xTWcikVCzQQqGJvdOpGsnN8xyJOSRhDbccvpzYIUL4GtBoc1rIpFQs0EKhib/rKPT7rAxyL6Oi0RhaLuNsGgA73h71sSsUCbSNQxZp/YjnInVxOBKh9WcHuBubPP/0xSsUJDQSq2PJPLAfwoPN93+RyApSrCjd9XLCTly1b+AIqFSO0akgVW/4NxC1kK8MS5gGnegkJQP0uBT/5a69Zi1JKA4Eqnga+uSJXA/GDCTN8Bo9ZewWa3VDwN/joI2tRSmkgUMVPoCoh/1QSxth3Az0m6LgBpQpJA4Eqdu77KD3Xtr7Ob3zHDAjWCGLtLqpUoWkgUMXKwDdXkBNg+qKOsu7UioCcVxuu+Gf4CqZUDNNAoIqN0akbc1UJgTV4rJrjd8+6AJQ5N3wFUyrGafdRVSykpmXmGjgGVnfRgIPHCptKYsmSwr0+gFINGxT5OZUKBw0EqlgIlEvoQef7Pt1FPUGgoIPHwqTyo49GughKnRENBCriAnUVHeBYxPBAYwbOq13wwWNKqXxpG4GKqLzaBYY4vwD8xwwAfd4IT8GUiiMaCFTEjE7dmGe7QB1HpmfdUyXUfqSOGVAqBLRqSEVEXo3DUxLG+DQOgx0ELvybdhdVKkQ0EKiwS03LZOSH6bm2z04YTQvnDsC3SkgALtcgoFSoaCBQYZVXdVC+QUCrhJQKKW0jUGGTVxCYkjAm/yCgVUJKhZTeEaiwGPjmioC9g/zvBNx5hKyEcq8Uy/ECSsUaDQQqpPJqDwD4IuEB6jt/BvyCQLmq0O8drQ5SKkw0EKiQyesuAKzqIO8g4Cbn1Ya708JRPKWUTQOBKnJ5tQW45Zp83n0nIKIDxpSKAA0EqsicLgC4+U8+j4CUTYTrP9DqIKUiQAOBKpTUtEzu+zCdnCCPDzT5vFYHKRVZGghUgeVX95+fAY5FgbOJanWQUhGlgUAFdMVLS9i2/68iO587pbT35PMCUL6GVgcpFWEhDQQi0hV4BXACbxljxvrtF3t/d+AoMNgYsy7XiQopNS2TUTPTORls/YUqMg863+cW5+eUFhfg20MIgA73h79QSikfIQsEIuIE/g1cAWQA34nIXGPMD16HdQPq2ktb4HX7sci4+7G3kK08mDCDRo6dlCIbBznk4CAHwYHxrANh3Rfp9w9luZ24cHp3DfUPAsn9dMCYUsVAKO8I2gDbjTE7AERkBtAb8A4EvYFpxhgDrBSRCiJSxRizr6gK8cKXW2ghW/mw5NMk5GrSdOWzHu59kX7/UJU7QAAAKwhc82aAHUqpcAtlIKgG7PVazyD3t/1Ax1QDfAKBiAwFhgLUqFGjQIX4+dAxejt/xElO4A8kFX6aP0ipYiWUgSDQx645g2MwxkwGJgO0atUq1/78VK1QhpV/NMSFAzHaSBByEviXCg64oDH0eEkbh5UqZkIZCDKA6l7rScDPZ3BMoYzqUp+RHx6j/4kneNCpbQRFXW6nw5DgsJPYmhwQh7WYHGspURZa36Z3AEoVY6EMBN8BdUWkFpAJDABu8DtmLnCn3X7QFvijKNsHAK5uXg2AUTNhQPYTRXnquNH+ovN47/aLI10MpVSIhCwQGGOyReRO4Eus7qNvG2M2icgwe/8kYD5W19HtWN1HbwlFWa5uXs0TEJRSSvkK6TgCY8x8rA97722TvJ4b4I5QlkEppVT+dIYypZSKcxoIlFIqzmkgUEqpOKeBQCml4pxY7bXRQ0QOALvP8OWJwMEiLE400GuOD3rN8aEw13yhMaZSoB1RFwgKQ0TWGGNaRboc4aTXHB/0muNDqK5Zq4aUUirOaSBQSqk4F2+BYHKkCxABes3xQa85PoTkmuOqjUAppVRu8XZHoJRSyo8GAqWUinMxGQhEpKuIbBGR7SLycID9IiL/Z+/fICItIlHOohTENQ+0r3WDiCwXkWaRKGdROt01ex3XWkRcInJtOMsXCsFcs4h0FJF0EdkkIl+Hu4xFLYi/7fIi8qmIrLevOSRZjMNFRN4Wkf0i8n0e+4v+88sYE1MLVsrrn4DaQElgPdDI75juwOdYk2m1A1ZFutxhuOa/Aefaz7vFwzV7HbcYKwvutZEudxh+zxWw5gWvYa+fH+lyh+GaHwWet59XAn4DSka67IW45kuBFsD3eewv8s+vWLwjaANsN8bsMMacAGYAvf2O6Q1MM5aVQAURqRLughah016zMWa5MeZ3e3Ul1mxw0SyY3zPAXcBsYH84CxciwVzzDcAcY8weAGNMtF93MNdsgHIiIsDZWIEgO7zFLDrGmKVY15CXIv/8isVAUA3Y67WeYW8r6DHRpKDXcyvWN4podtprFpFqQB9gErEhmN9zPeBcEVkiImtF5KawlS40grnmiUBDrGluNwL3GBPTE5QX+edXSCemiZBAc6f795EN5phoEvT1iEgnrEBwSUhLFHrBXPME4CFjjMv6shj1grnmBKAl0BkoA6wQkZXGmK2hLlyIBHPNXYB04DLgImCBiHxjjDkc4rJFSpF/fsViIMgAqnutJ2F9UyjoMdEkqOsRkabAW0A3Y8yvYSpbqARzza2AGXYQSAS6i0i2MSY1LCUsesH+bR80xvwF/CUiS4FmQLQGgmCu+RZgrLEq0LeLyE6gAbA6PEUMuyL//IrFqqHvgLoiUktESgIDgLl+x8wFbrJb39sBfxhj9oW7oEXotNcsIjWAOcCNUfzt0Ntpr9kYU8sYU9MYUxOYBYyI4iAAwf1tfwJ0EJEEESkLtAV+DHM5i1Iw17wH6w4IEbkAqA/sCGspw6vIP79i7o7AGJMtIncCX2L1OHjbGLNJRIbZ+ydh9SDpDmwHjmJ9o4haQV7zE0BF4DX7G3K2ieLMjUFec0wJ5pqNMT+KyBfABiAHeMsYE7AbYjQI8vf8L2CqiGzEqjZ5yBgTtempReQDoCOQKCIZwJNACQjd55emmFBKqTgXi1VDSimlCkADgVJKxTkNBEopFec0ECilVJzTQKCUUnFOA0GMEpGRdj/yIjkuiPM8LSKXB9jeUUTmFfb8+bxvioh0D9G5p4rITjuT52YRedJr31si0igU7+tXhjvtLJNGRBK9tueZgTKvbJ0icp6ILBCRbfbjuWdYprD+bdnn6iYia0TkR/t3Md5r31B722YRWS0i0T5qPvwinWlPl9AswC4gsaiOK0Q5OgLzQnj+wcDEEJ17KnbGUqA01iClWmH+PTYHavr/nsgjAyX5ZOsExgEP288fxs7YWdz/toAm9jU1sNcTsAYHAvQA1rrfBytr5x6gcjh/T9G+RLwAuhTyFwhnAZ/Z/+G/B/oDdwMnsBJwfWUf9zqwBtgE/NPeFui4K4EVwDpgJlY2xzZYGS3Bynx4zP6QKQ3ssLd7f2h2BTYD3wL/5w4Edlnfxhotmgb0DnA9HwLdvdanAtfY7zXFLmsa0Mkuwx7gAFaumf7BvIff+9XEGnn7pv2z+S9QJsA1VcAKBOfb60uAVvbzI8Cz9u9gJXCBvf06+3eyHlhayN+zz4cq8AZwvdf6FqAKcDHwpdf2R4BHvI+xn1cBtkT6byvIa58GDMlj3zfAZX7b/gX8K9L/N6NpiXgBdCnkL9D6kHzTa728/ej/wXGe/ei0P8Sa+h+HlY9nKXCWvf4Q1ojkBGCnvW28/SHbHvg78IG9fSpwLdYH9l6gLta31Y84FQieAwbZzytg5b85y+96+gDv2M9L2ucqA9wPTLG3N8AKAKXxuyMI5j383q8mVsriFHv9I6/XTwV2YgWZI8BzXq9bwqlAYICe9vNxwGj7+UagmrssAd67nH3uQIt/zn3/3+c84BKv9UVYuZWuxRpN7N5+o/vnAxzyO+fvkf7bsp+/nMfPwH33sg5olkcZf3OXy2tbb+wvLroEt8Rciok4tBEYLyLPY33gfpPHcf1EZCjWh3oVoBFWGgJv7ezty+w0FCWBFcYa5r9dRBpi3R28hDV5hhPrG5m3BlhBYxuAiEwHhtr7rgR6icgD9nppoAa+uXA+B/5PREph3VksNcYcs+t9XwUwxmwWkd1YKZf9BfMe/nYaY9Lt52uxgoPbKGPMLBE5G1gkIn8zxiz3e/0JrA9m9+uvsJ8vw0p98BFWnicfxpg/gZR8ypWfvDJQFmVmypD/bQEYY+49w/LlRYjubMJhp4EgyhljtopIS6w64zEi8l9jzNPex4hILeABoLUx5ncRmYr1AelPgAXGmOsD7PsGa2azk8BCrG/LTvu8uYqVR3EFuMYYsyWf68kSkSVYqYX7Ax94vTYYp32PAI57PXdh3YH4l+uIXa5LAP9AcNLYX0Xt1yfYrxkmIm2Bq4B0EUkxXllfRaQcuQOp2w3GmB/yKXNeGShL5rEd4BcRqWKM2SfWRCb5TloTrr8tEXkZq6rP3wxjzFisKqeWWFVU/n6w9y322tbC3q6CpL2GopyIVAWOGmOmY1XbuHuP/IlV9QBwDvAX8IednbGb1ym8j1sJtBeROva5y4qI+1v3UmAk1h3CAawEdg2w/pN62wzUEpGL7HXv//hfAneJ/ZVQRJrncVkzsBJpdbBf437/gfbr6mF9y9/iV/4830NEqonIojze77REJAErk+dPBXjNRcaYVcaYJ4CD+H5AY4z50xiTksdyug+yvDJQ5petcy5ws/38ZqxMpXn+bML1t2WMuTePn8FY+7UvAI+6jxcRh4jcZ+8bBzwvIhXtfSlY1YWvnebnp7zoHUH0SwZeEJEcrG/rw+3tk4HPRWSfMaaTiKRhfWjvwKqyII/jBgMf2FUzAKOx6tlXARdgfSCDdeu/3+ubMOD5Rj8U+ExEDmI1GDexd/8La7KYDfYH9S6sXh/+/ovVQDjXWNMTgvUfe5JYGSazgcHGmOMi8hXwsIikA2PyeY8qnNn0hS+IyGisb9qLCFDFc5rXuttKFhH4G22+RORu4EGgMtY1zTfG3EYeGShNHtk67dONBT4SkVux2lius7fn9bMJ199WvowxG0RkpP3aslh3nJ/Z++aKNRPdchExWMFnkInutPJhp9lHVVywPxz3GGP8c9nHPf3ZKA0ESikV57SNQCml4pwGAqWUinMaCJRSKs5pIFBKqTingUAppeKcBgKllIpz/w/8SZ5JKGlsOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.525 3.17963\n",
      "0.33426 fractional expected GOP seats =  2.674  out of  8 seats\n",
      "simpler expected GOP seats =  2.8862  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "plt.show()\n",
    "\n",
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\", state=\"+str(STATE), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",r3(Rsimple) ,r5(Rsmeared) )   \n",
    "print(r5(smearedSeats),\"fractional expected GOP seats = \",r3(nDistricts*smearedSeats),\" out of \",nDistricts,\"seats\")\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5793698b-d681-4b37-a954-29937a12f61c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3108\n"
     ]
    }
   ],
   "source": [
    "#plot tractUse here ...\n",
    "print(nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "74a657df-47e5-456d-9ee4-81690ec2b8f1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On a cold re-start, we need to regenerate the countyGeoms and neighborLists\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_53/1687193767.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     10\u001b[0m                 \u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m                 \u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcountyGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     13\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"countyGeoms regenerated, now build the neighborlists\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0;31m# start with county neighbor list for efficiency\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/geometry/base.py\u001b[0m in \u001b[0;36munion\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    696\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0munion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;34m\"\"\"Returns the union of the geometries (Shapely geometry)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 698\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgeom_factory\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mimpl\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'union'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    699\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    700\u001b[0m     \u001b[0;31m# Unary predicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/shapely/topology.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, this, other, *args)\u001b[0m\n\u001b[1;32m     66\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstop_prepared\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m         \u001b[0mproduct\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_geom\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mproduct\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     70\u001b[0m             err = TopologicalError(\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"On a cold re-start, we need to regenerate the countyGeoms and neighborLists\")\n",
    "countyGeom = [dummyPoly]*nCounties\n",
    "countyTractList = [[]] * nCounties\n",
    "for c in range(nCounties):  #nCounties\n",
    "    countyTractList[c] = []\n",
    "    for t in range(nTracts):\n",
    "        if countyNo[t] == c:\n",
    "            countyTractList[c].append(t)\n",
    "            if countyGeom[c] == dummyPoly:\n",
    "                countyGeom[c] = tractGeom[t]\n",
    "            else:\n",
    "                countyGeom[c] = countyGeom[c].union(tractGeom[t])\n",
    "print(\"countyGeoms regenerated, now build the neighborlists\")\n",
    "# start with county neighbor list for efficiency\n",
    "\n",
    "neighborCountyList = [-999]*nCounties\n",
    "for c in range(nCounties):\n",
    "    neighborCountyList[c] = []\n",
    "for c in range(nCounties):\n",
    "    for cc in range(c+1,nCounties):\n",
    "        if c != cc :  \n",
    "            if countyGeom[c].intersects(countyGeom[cc]) :\n",
    "                neighborCountyList[c].append(cc)\n",
    "                neighborCountyList[cc].append(c)\n",
    "print(\"done with county neighbors\")\n",
    "\n",
    "neighborList = [\"empty\"]*nTracts\n",
    "for p in range(nTracts):  #this loop takes about 3 minutes for 9000 tracts\n",
    "    neighborList[p] = []\n",
    "for p in range(nTracts):\n",
    "    if p%400 == 0:\n",
    "        print(\"working on census vtd no\",p)\n",
    "    for pp in countyTractList[countyNo[p]] : #for efficiency, look only in home county and ...\n",
    "        if tractGeom[p].intersects(tractGeom[pp]) and p != pp:\n",
    "            neighborList[p].append(pp)\n",
    "    for cc in neighborCountyList[countyNo[p]]:  #... and neighboring counties\n",
    "        for pp in countyTractList[cc]:\n",
    "            if tractGeom[p].intersects(tractGeom[pp]):\n",
    "                neighborList[p].append(pp)\n",
    "print(\"all done finding each county's and each vtd's neighbors\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "355d0e12-6742-4c92-a009-ffdc84e9146c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ON A COLD RESTART ONLY, we need to read in each Home District's list of used tracts, and other stats\n",
      "I converted each HDtractList to a list.  Now I'll compute each tract's usage across all Home Districts\n",
      "all done computing tract usages.  Cold re-start complete\n"
     ]
    }
   ],
   "source": [
    "# COLD RESTART WITH NEIGHBOR LIST *******\n",
    "nDistricts = 8  #CO CONGRESSIONAL\n",
    "avgDistrictPop = np.sum(tractPop)/nDistricts\n",
    "print(\"ON A COLD RESTART ONLY, we need to read in each Home District's list of used tracts, and other stats\")\n",
    "infilename = \"CO8HDpcL0.36lLmAR1.914Sepunpatched.csv\"\n",
    "df1 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDArea = df1['HDarea']\n",
    "HDvGOP = df1['HDvGOP']\n",
    "HDtractListString = df1['HDtractList']\n",
    "partialTractNo = df1['partialNo']\n",
    "partialTractUse = df1['partialUse']\n",
    "vtdTrump = df1['vtdTrump']\n",
    "vtdBiden = df1['vtdBiden']\n",
    "#tractCPx = df1['centroid x']   #UNCOMMENT THESE IF WE DID NOT PULL WITH TRACT GEOMETRIES\n",
    "#tractCPy = df1['centroid y']\n",
    "#tractCP = [Point(0,0)]*nTracts\n",
    "#for t in range(nTracts):\n",
    "#    tractCP[t] = Point(tractCPx[t],tractCPy[t])\n",
    "neighborListString = df1['neighborList']   #just now implemented.  Uncomment if not implemented\n",
    "\n",
    "HDvGOP = HDvGOP.to_numpy()\n",
    "partialTractUse = partialTractUse.to_numpy()\n",
    "import ast\n",
    "HDtractList = [[]]*nTracts  #convert to list, as these read in as strings\n",
    "neighborList = [[]]*nTracts  #convert to list, as these read in as strings\n",
    "for t in range(nTracts):\n",
    "    if HDtractListString[t] != \"[]\" :  #special handling for empty lists (nonpopd tracts)\n",
    "        HDtractList[t] = ast.literal_eval(HDtractListString[t])\n",
    "    if neighborListString[t] != \"[]\" :  #special handling for empty lists (nonpopd tracts)\n",
    "        neighborList[t] = ast.literal_eval(neighborListString[t])\n",
    "\n",
    "print(\"I converted each HDtractList to a list.  Now I'll compute each tract's usage across all Home Districts\")\n",
    "tractUse = [0.]*nTracts\n",
    "for tt in range(nTracts):\n",
    "    for t in HDtractList[tt]:\n",
    "        tractUse[t] += tractPop[tt]/avgDistrictPop\n",
    "    tractUse[partialTractNo[tt]] -= (1. - partialTractUse[tt])*tractPop[tt]/avgDistrictPop\n",
    "print(\"all done computing tract usages.  Cold re-start complete\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "2fd85a59-2395-40db-829f-65b36501944a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "and plot the tract usage distro\n",
      "1.0 0.21141 = avg,std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"and plot the tract usage distro\")\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/np.sum(tractPop)\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 * tractPop[t]/np.sum(tractPop)\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "print(r3(avgTractUse),r5(sdTractUse),\"= avg,std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "cc20d967-db76-44fd-a19e-51d3e52f71b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are 0.7 1.6\n",
      "We do not plot tracts with usage between 0.9 and 1.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 135 used < 0.7 and 113 used more than 1.6\n"
     ]
    }
   ],
   "source": [
    "\n",
    "minUse = 0.7\n",
    "maxUse = 1.6\n",
    "minNoPlot = 0.9\n",
    "maxNoPlot = 1.1\n",
    "print(\"Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are\",minUse, maxUse)\n",
    "print(\"We do not plot tracts with usage between\",minNoPlot,\"and\",maxNoPlot)\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minNoPlot:\n",
    "        redd = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        gren = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        bluu = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy\n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor=(redd,gren,bluu) )\n",
    "    if tractUse[t] > maxNoPlot:\n",
    "        redd = 1\n",
    "        gren = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        bluu = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy            \n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "MAPboundary = Polygon([Point(-109,37),Point(-102.06,37),Point(-102.06,41),Point(-109,41)])\n",
    "MAPboundary = MAPboundary.buffer(0.01)\n",
    "plotPoly(MAPboundary)\n",
    "plt.show()\n",
    "nWayOverusedTracts = 0\n",
    "nWayUnderusedTracts = 0\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minUse:\n",
    "        nWayUnderusedTracts +=1\n",
    "    if tractUse[t] > maxUse:\n",
    "        nWayOverusedTracts +=1\n",
    "print(\"there are a total of\",nWayUnderusedTracts,\"used <\",minUse,\"and\",nWayOverusedTracts,\"used more than\",maxUse)        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b56da923-46e2-42cd-9cfd-b9caa4d4200c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8044167064735105 2.1504951578827765\n"
     ]
    }
   ],
   "source": [
    "maxT = tractUse.index(np.max(tractUse))\n",
    "NLIST = neighborList[maxT]\n",
    "boundaryUse = 0.\n",
    "for t in range(nTracts):\n",
    "    isBoundary = \"no\"\n",
    "    if maxT in HDtractList[t]:\n",
    "        for n in NLIST:\n",
    "            if n not in HDtractList[t]:\n",
    "                isBoundary = \"yes\"\n",
    "    if isBoundary == \"yes\":\n",
    "        plotPoly(tractGeom[t])\n",
    "        frac = 1.\n",
    "        if partialTractNo[t] == maxT:\n",
    "            frac = partialTractUse[t]\n",
    "        boundaryUse += frac * tractPop[t]/avgDistrictPop\n",
    "plt.show()\n",
    "print(boundaryUse, tractUse[maxT])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d731d30f-9dbb-4a26-b789-a5b3f117e284",
   "metadata": {},
   "outputs": [],
   "source": [
    "def getFrac(t,partialNo, partialUse):\n",
    "    frac = 1.\n",
    "    if partialNo == t:\n",
    "        frac = partialUse\n",
    "    result = frac \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8022b71f-696d-4a27-ba5f-14db4f326762",
   "metadata": {},
   "outputs": [],
   "source": [
    "#BELOW CODE IS FOR REDUCING TRACT OVERUSE.  COULD ALSO DEVELOP A CODE FOR INCREASING TRACT USE FOR UNDERUSED TRACTS\n",
    "#see vtdCO for latest approach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "7ae7837d-2a11-4d4c-9cd4-504d2c748625",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This code block aims to narrow the breadth of the precinct usage distribution\n",
      "the most overused tract 1740 with pop 310 and usage 2.1504951578827765\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 42142.0 OUT and t0 usage now 2.137 1.372\n",
      "all done exchanging OUTs for UUTs for tract 987 . Total popGained = 26249.0 OUT and t0 usage now 2.125 1.372\n",
      "all done exchanging OUTs for UUTs for tract 990 . Total popGained = 27105.0 OUT and t0 usage now 2.114 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2276 . Total popGained = 26115.0 OUT and t0 usage now 2.104 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2263 . Total popGained = 25849.0 OUT and t0 usage now 2.097 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 41896.0 OUT and t0 usage now 2.085 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 42350.0 OUT and t0 usage now 2.075 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 43077.0 OUT and t0 usage now 2.065 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 42415.0 OUT and t0 usage now 2.05 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 43894.0 OUT and t0 usage now 2.038 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 42204.0 OUT and t0 usage now 2.018 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 41711.0 OUT and t0 usage now 1.999 1.372\n",
      "all done exchanging OUTs for UUTs for tract 782 . Total popGained = 40860.0 OUT and t0 usage now 1.975 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 42205.0 OUT and t0 usage now 1.953 1.372\n",
      "all done exchanging OUTs for UUTs for tract 788 . Total popGained = 41842.0 OUT and t0 usage now 1.932 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 37942.0 OUT and t0 usage now 1.914 1.372\n",
      "all done exchanging OUTs for UUTs for tract 89 . Total popGained = 32144.0 OUT and t0 usage now 1.897 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 39830.0 OUT and t0 usage now 1.883 1.372\n",
      "all done exchanging OUTs for UUTs for tract 674 . Total popGained = 38085.0 OUT and t0 usage now 1.868 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2305 . Total popGained = 26407.0 OUT and t0 usage now 1.857 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2325 . Total popGained = 27340.0 OUT and t0 usage now 1.848 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2288 . Total popGained = 35708.0 OUT and t0 usage now 1.838 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2302 . Total popGained = 36465.0 OUT and t0 usage now 1.828 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2347 . Total popGained = 33739.0 OUT and t0 usage now 1.821 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2351 . Total popGained = 36906.0 OUT and t0 usage now 1.812 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2379 . Total popGained = 36719.0 OUT and t0 usage now 1.805 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2369 . Total popGained = 33740.0 OUT and t0 usage now 1.795 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2360 . Total popGained = 26846.0 OUT and t0 usage now 1.789 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2358 . Total popGained = 34671.0 OUT and t0 usage now 1.782 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2333 . Total popGained = 35321.0 OUT and t0 usage now 1.773 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2353 . Total popGained = 33947.0 OUT and t0 usage now 1.765 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2329 . Total popGained = 36012.0 OUT and t0 usage now 1.754 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 34524.0 OUT and t0 usage now 1.747 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2371 . Total popGained = 34105.0 OUT and t0 usage now 1.739 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2298 . Total popGained = 20574.0 OUT and t0 usage now 1.728 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2315 . Total popGained = 34487.0 OUT and t0 usage now 1.718 1.372\n",
      "all done exchanging OUTs for UUTs for tract 68 . Total popGained = 34554.0 OUT and t0 usage now 1.707 1.372\n",
      "all done exchanging OUTs for UUTs for tract 70 . Total popGained = 26758.0 OUT and t0 usage now 1.7 1.372\n",
      "all done exchanging OUTs for UUTs for tract 28 . Total popGained = 35164.0 OUT and t0 usage now 1.686 1.372\n",
      "all done exchanging OUTs for UUTs for tract 661 . Total popGained = 34804.0 OUT and t0 usage now 1.677 1.372\n",
      "all done exchanging OUTs for UUTs for tract 27 . Total popGained = 33386.0 OUT and t0 usage now 1.666 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1694 . Total popGained = 33868.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2308 . Total popGained = 34107.0 OUT and t0 usage now 1.642 1.372\n",
      "all done exchanging OUTs for UUTs for tract 652 . Total popGained = 34672.0 OUT and t0 usage now 1.629 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 35448.0 OUT and t0 usage now 1.616 1.372\n",
      "all done exchanging OUTs for UUTs for tract 818 . Total popGained = 26234.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1687 . Total popGained = 33042.0 OUT and t0 usage now 1.588 1.372\n",
      "all done exchanging OUTs for UUTs for tract 761 . Total popGained = 21625.0 OUT and t0 usage now 1.577 1.372\n",
      "all done exchanging OUTs for UUTs for tract 651 . Total popGained = 34156.0 OUT and t0 usage now 1.563 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1156 . Total popGained = 16374.0 OUT and t0 usage now 1.528 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1096 . Total popGained = 26189.0 OUT and t0 usage now 1.513 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1038 . Total popGained = 23424.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1170 . Total popGained = 23274.0 OUT and t0 usage now 1.49 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1168 . Total popGained = 22868.0 OUT and t0 usage now 1.478 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1175 . Total popGained = 22741.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1176 . Total popGained = 22741.0 OUT and t0 usage now 1.452 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1179 . Total popGained = 23445.0 OUT and t0 usage now 1.437 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1061 . Total popGained = 23874.0 OUT and t0 usage now 1.425 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 29816.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1092 . Total popGained = 25674.0 OUT and t0 usage now 1.4 1.372\n",
      "we have now reduced 1740 usage to 1.3984066408554345 . Time for next OUT\n",
      "the most overused tract 209 with pop 1206 and usage 2.078252623353342\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 15663.0 OUT and t0 usage now 2.067 1.372\n",
      "all done exchanging OUTs for UUTs for tract 987 . Total popGained = 8602.0 OUT and t0 usage now 2.055 1.372\n",
      "all done exchanging OUTs for UUTs for tract 990 . Total popGained = 10670.0 OUT and t0 usage now 2.046 1.372\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 15085.0 OUT and t0 usage now 2.038 1.372\n",
      "all done exchanging OUTs for UUTs for tract 808 . Total popGained = 15392.0 OUT and t0 usage now 2.027 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2273 . Total popGained = 16556.0 OUT and t0 usage now 2.02 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2280 . Total popGained = 11827.0 OUT and t0 usage now 2.01 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2262 . Total popGained = 17220.0 OUT and t0 usage now 1.996 1.372\n",
      "all done exchanging OUTs for UUTs for tract 268 . Total popGained = 6224.0 OUT and t0 usage now 1.984 1.372\n",
      "all done exchanging OUTs for UUTs for tract 812 . Total popGained = 16999.0 OUT and t0 usage now 1.971 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 15263.0 OUT and t0 usage now 1.958 1.372\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 15205.0 OUT and t0 usage now 1.946 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2255 . Total popGained = 17968.0 OUT and t0 usage now 1.933 1.372\n",
      "all done exchanging OUTs for UUTs for tract 801 . Total popGained = 15796.0 OUT and t0 usage now 1.924 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2256 . Total popGained = 15079.0 OUT and t0 usage now 1.903 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2258 . Total popGained = 11735.0 OUT and t0 usage now 1.886 1.372\n",
      "all done exchanging OUTs for UUTs for tract 782 . Total popGained = 16159.0 OUT and t0 usage now 1.862 1.372\n",
      "all done exchanging OUTs for UUTs for tract 793 . Total popGained = 16250.0 OUT and t0 usage now 1.84 1.372\n",
      "all done exchanging OUTs for UUTs for tract 788 . Total popGained = 15084.0 OUT and t0 usage now 1.819 1.372\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 15952.0 OUT and t0 usage now 1.801 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 15904.0 OUT and t0 usage now 1.779 1.372\n",
      "all done exchanging OUTs for UUTs for tract 86 . Total popGained = 14995.0 OUT and t0 usage now 1.767 1.372\n",
      "all done exchanging OUTs for UUTs for tract 958 . Total popGained = 15731.0 OUT and t0 usage now 1.758 1.372\n",
      "all done exchanging OUTs for UUTs for tract 88 . Total popGained = 6279.0 OUT and t0 usage now 1.748 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1933 . Total popGained = 15728.0 OUT and t0 usage now 1.739 1.372\n",
      "all done exchanging OUTs for UUTs for tract 17 . Total popGained = 16587.0 OUT and t0 usage now 1.73 1.372\n",
      "all done exchanging OUTs for UUTs for tract 771 . Total popGained = 15372.0 OUT and t0 usage now 1.72 1.372\n",
      "all done exchanging OUTs for UUTs for tract 657 . Total popGained = 15044.0 OUT and t0 usage now 1.711 1.372\n",
      "all done exchanging OUTs for UUTs for tract 673 . Total popGained = 11868.0 OUT and t0 usage now 1.698 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 16415.0 OUT and t0 usage now 1.686 1.372\n",
      "all done exchanging OUTs for UUTs for tract 653 . Total popGained = 15030.0 OUT and t0 usage now 1.674 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 14822.0 OUT and t0 usage now 1.661 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2321 . Total popGained = 20760.0 OUT and t0 usage now 1.651 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2299 . Total popGained = 16313.0 OUT and t0 usage now 1.642 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2300 . Total popGained = 16643.0 OUT and t0 usage now 1.633 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2340 . Total popGained = 14292.0 OUT and t0 usage now 1.621 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2322 . Total popGained = 8404.0 OUT and t0 usage now 1.612 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2289 . Total popGained = 15365.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2374 . Total popGained = 15877.0 OUT and t0 usage now 1.593 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 11244.0 OUT and t0 usage now 1.585 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 15022.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 17130.0 OUT and t0 usage now 1.568 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2362 . Total popGained = 15786.0 OUT and t0 usage now 1.558 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2335 . Total popGained = 14259.0 OUT and t0 usage now 1.551 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2359 . Total popGained = 14362.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2291 . Total popGained = 12969.0 OUT and t0 usage now 1.534 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 16033.0 OUT and t0 usage now 1.528 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2355 . Total popGained = 16410.0 OUT and t0 usage now 1.517 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 15976.0 OUT and t0 usage now 1.508 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 13897.0 OUT and t0 usage now 1.501 1.372\n",
      "all done exchanging OUTs for UUTs for tract 41 . Total popGained = 22614.0 OUT and t0 usage now 1.486 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2296 . Total popGained = 14807.0 OUT and t0 usage now 1.475 1.372\n",
      "all done exchanging OUTs for UUTs for tract 672 . Total popGained = 24900.0 OUT and t0 usage now 1.465 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2310 . Total popGained = 12754.0 OUT and t0 usage now 1.454 1.372\n",
      "all done exchanging OUTs for UUTs for tract 772 . Total popGained = 25875.0 OUT and t0 usage now 1.438 1.372\n",
      "all done exchanging OUTs for UUTs for tract 29 . Total popGained = 13661.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1828 . Total popGained = 25151.0 OUT and t0 usage now 1.416 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2308 . Total popGained = 16273.0 OUT and t0 usage now 1.406 1.372\n",
      "we have now reduced 209 usage to 1.3964224760699968 . Time for next OUT\n",
      "the most overused tract 285 with pop 2053 and usage 1.997268645382943\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 6436.0 OUT and t0 usage now 1.986 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 6900.0 OUT and t0 usage now 1.977 1.372\n",
      "all done exchanging OUTs for UUTs for tract 668 . Total popGained = 5937.0 OUT and t0 usage now 1.968 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2268 . Total popGained = 1989.0 OUT and t0 usage now 1.96 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 7037.0 OUT and t0 usage now 1.95 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2257 . Total popGained = 1293.0 OUT and t0 usage now 1.938 1.372\n",
      "all done exchanging OUTs for UUTs for tract 669 . Total popGained = 7581.0 OUT and t0 usage now 1.925 1.372\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 5884.0 OUT and t0 usage now 1.912 1.372\n",
      "all done exchanging OUTs for UUTs for tract 801 . Total popGained = 8682.0 OUT and t0 usage now 1.899 1.372\n",
      "all done exchanging OUTs for UUTs for tract 796 . Total popGained = 6177.0 OUT and t0 usage now 1.88 1.372\n",
      "all done exchanging OUTs for UUTs for tract 785 . Total popGained = 6057.0 OUT and t0 usage now 1.857 1.372\n",
      "all done exchanging OUTs for UUTs for tract 784 . Total popGained = 6192.0 OUT and t0 usage now 1.83 1.372\n",
      "all done exchanging OUTs for UUTs for tract 786 . Total popGained = 6149.0 OUT and t0 usage now 1.811 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 6569.0 OUT and t0 usage now 1.787 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 7330.0 OUT and t0 usage now 1.765 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1104 . Total popGained = 7424.0 OUT and t0 usage now 1.755 1.372\n",
      "all done exchanging OUTs for UUTs for tract 953 . Total popGained = 6151.0 OUT and t0 usage now 1.743 1.372\n",
      "all done exchanging OUTs for UUTs for tract 25 . Total popGained = 5225.0 OUT and t0 usage now 1.735 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1691 . Total popGained = 7037.0 OUT and t0 usage now 1.727 1.372\n",
      "all done exchanging OUTs for UUTs for tract 661 . Total popGained = 8291.0 OUT and t0 usage now 1.716 1.372\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 6164.0 OUT and t0 usage now 1.703 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 6910.0 OUT and t0 usage now 1.694 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1684 . Total popGained = 7388.0 OUT and t0 usage now 1.681 1.372\n",
      "all done exchanging OUTs for UUTs for tract 650 . Total popGained = 7316.0 OUT and t0 usage now 1.669 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1685 . Total popGained = 6701.0 OUT and t0 usage now 1.659 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1690 . Total popGained = 5448.0 OUT and t0 usage now 1.646 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1686 . Total popGained = 7165.0 OUT and t0 usage now 1.634 1.372\n",
      "all done exchanging OUTs for UUTs for tract 100 . Total popGained = 7052.0 OUT and t0 usage now 1.625 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2346 . Total popGained = 8563.0 OUT and t0 usage now 1.615 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2324 . Total popGained = 4688.0 OUT and t0 usage now 1.606 1.372\n",
      "all done exchanging OUTs for UUTs for tract 101 . Total popGained = 6638.0 OUT and t0 usage now 1.598 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1689 . Total popGained = 4791.0 OUT and t0 usage now 1.589 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2383 . Total popGained = 2041.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 5667.0 OUT and t0 usage now 1.572 1.372\n",
      "all done exchanging OUTs for UUTs for tract 672 . Total popGained = 6815.0 OUT and t0 usage now 1.564 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2362 . Total popGained = 4683.0 OUT and t0 usage now 1.554 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2363 . Total popGained = 5482.0 OUT and t0 usage now 1.548 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2333 . Total popGained = 5195.0 OUT and t0 usage now 1.54 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2345 . Total popGained = 7016.0 OUT and t0 usage now 1.53 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2329 . Total popGained = 5235.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 5480.0 OUT and t0 usage now 1.513 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 1560.0 OUT and t0 usage now 1.507 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2301 . Total popGained = 5074.0 OUT and t0 usage now 1.493 1.372\n",
      "all done exchanging OUTs for UUTs for tract 772 . Total popGained = 6676.0 OUT and t0 usage now 1.482 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2294 . Total popGained = 4860.0 OUT and t0 usage now 1.468 1.372\n",
      "all done exchanging OUTs for UUTs for tract 30 . Total popGained = 4992.0 OUT and t0 usage now 1.456 1.372\n",
      "all done exchanging OUTs for UUTs for tract 29 . Total popGained = 1748.0 OUT and t0 usage now 1.446 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1700 . Total popGained = 17040.0 OUT and t0 usage now 1.435 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 6722.0 OUT and t0 usage now 1.425 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1832 . Total popGained = 16815.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 285 usage to 1.3992961895930434 . Time for next OUT\n",
      "the most overused tract 1749 with pop 3232 and usage 1.9942532692859403\n",
      "all done exchanging OUTs for UUTs for tract 268 . Total popGained = 12159.0 OUT and t0 usage now 1.983 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2041 . Total popGained = 14719.0 OUT and t0 usage now 1.971 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2260 . Total popGained = 15343.0 OUT and t0 usage now 1.967 1.372\n",
      "all done exchanging OUTs for UUTs for tract 808 . Total popGained = 15223.0 OUT and t0 usage now 1.955 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2300 . Total popGained = 15416.0 OUT and t0 usage now 1.95 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2318 . Total popGained = 16025.0 OUT and t0 usage now 1.944 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2386 . Total popGained = 16898.0 OUT and t0 usage now 1.935 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2374 . Total popGained = 17727.0 OUT and t0 usage now 1.926 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 15077.0 OUT and t0 usage now 1.918 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 14792.0 OUT and t0 usage now 1.91 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2330 . Total popGained = 15285.0 OUT and t0 usage now 1.901 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2368 . Total popGained = 21289.0 OUT and t0 usage now 1.893 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2360 . Total popGained = 14234.0 OUT and t0 usage now 1.884 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2359 . Total popGained = 12743.0 OUT and t0 usage now 1.876 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2367 . Total popGained = 15954.0 OUT and t0 usage now 1.866 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 12886.0 OUT and t0 usage now 1.859 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2355 . Total popGained = 15093.0 OUT and t0 usage now 1.849 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2293 . Total popGained = 14405.0 OUT and t0 usage now 1.841 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 13133.0 OUT and t0 usage now 1.831 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 16227.0 OUT and t0 usage now 1.817 1.372\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 15421.0 OUT and t0 usage now 1.806 1.372\n",
      "all done exchanging OUTs for UUTs for tract 802 . Total popGained = 16628.0 OUT and t0 usage now 1.797 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2294 . Total popGained = 13982.0 OUT and t0 usage now 1.786 1.372\n",
      "all done exchanging OUTs for UUTs for tract 773 . Total popGained = 14439.0 OUT and t0 usage now 1.769 1.372\n",
      "all done exchanging OUTs for UUTs for tract 807 . Total popGained = 15182.0 OUT and t0 usage now 1.756 1.372\n",
      "all done exchanging OUTs for UUTs for tract 796 . Total popGained = 14573.0 OUT and t0 usage now 1.744 1.372\n",
      "all done exchanging OUTs for UUTs for tract 814 . Total popGained = 15601.0 OUT and t0 usage now 1.719 1.372\n",
      "all done exchanging OUTs for UUTs for tract 781 . Total popGained = 14863.0 OUT and t0 usage now 1.701 1.372\n",
      "all done exchanging OUTs for UUTs for tract 955 . Total popGained = 14535.0 OUT and t0 usage now 1.678 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 790 . Total popGained = 15191.0 OUT and t0 usage now 1.655 1.372\n",
      "all done exchanging OUTs for UUTs for tract 956 . Total popGained = 14767.0 OUT and t0 usage now 1.639 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 14363.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 14551.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 854 . Total popGained = 23152.0 OUT and t0 usage now 1.592 1.372\n",
      "all done exchanging OUTs for UUTs for tract 765 . Total popGained = 14112.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 661 . Total popGained = 8787.0 OUT and t0 usage now 1.57 1.372\n",
      "all done exchanging OUTs for UUTs for tract 763 . Total popGained = 21549.0 OUT and t0 usage now 1.561 1.372\n",
      "all done exchanging OUTs for UUTs for tract 817 . Total popGained = 18268.0 OUT and t0 usage now 1.546 1.372\n",
      "all done exchanging OUTs for UUTs for tract 659 . Total popGained = 14902.0 OUT and t0 usage now 1.531 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 14595.0 OUT and t0 usage now 1.517 1.372\n",
      "all done exchanging OUTs for UUTs for tract 666 . Total popGained = 14671.0 OUT and t0 usage now 1.503 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2784 . Total popGained = 17789.0 OUT and t0 usage now 1.489 1.372\n",
      "all done exchanging OUTs for UUTs for tract 653 . Total popGained = 14481.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 13434.0 OUT and t0 usage now 1.459 1.372\n",
      "all done exchanging OUTs for UUTs for tract 737 . Total popGained = 16520.0 OUT and t0 usage now 1.445 1.372\n",
      "all done exchanging OUTs for UUTs for tract 752 . Total popGained = 14806.0 OUT and t0 usage now 1.436 1.372\n",
      "all done exchanging OUTs for UUTs for tract 748 . Total popGained = 16133.0 OUT and t0 usage now 1.417 1.372\n",
      "all done exchanging OUTs for UUTs for tract 738 . Total popGained = 16531.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 1749 usage to 1.3994804730542583 . Time for next OUT\n",
      "the most overused tract 296 with pop 1066 and usage 1.990817002712639\n",
      "all done exchanging OUTs for UUTs for tract 2039 . Total popGained = 12603.0 OUT and t0 usage now 1.979 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 12486.0 OUT and t0 usage now 1.971 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2260 . Total popGained = 5559.0 OUT and t0 usage now 1.964 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 13459.0 OUT and t0 usage now 1.953 1.372\n",
      "all done exchanging OUTs for UUTs for tract 670 . Total popGained = 12741.0 OUT and t0 usage now 1.947 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 14194.0 OUT and t0 usage now 1.93 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 13022.0 OUT and t0 usage now 1.918 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 13826.0 OUT and t0 usage now 1.898 1.372\n",
      "all done exchanging OUTs for UUTs for tract 813 . Total popGained = 13105.0 OUT and t0 usage now 1.884 1.372\n",
      "all done exchanging OUTs for UUTs for tract 814 . Total popGained = 12452.0 OUT and t0 usage now 1.86 1.372\n",
      "all done exchanging OUTs for UUTs for tract 806 . Total popGained = 13511.0 OUT and t0 usage now 1.836 1.372\n",
      "all done exchanging OUTs for UUTs for tract 89 . Total popGained = 9350.0 OUT and t0 usage now 1.821 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 14110.0 OUT and t0 usage now 1.797 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 12808.0 OUT and t0 usage now 1.772 1.372\n",
      "all done exchanging OUTs for UUTs for tract 957 . Total popGained = 12506.0 OUT and t0 usage now 1.756 1.372\n",
      "all done exchanging OUTs for UUTs for tract 953 . Total popGained = 13699.0 OUT and t0 usage now 1.746 1.372\n",
      "all done exchanging OUTs for UUTs for tract 663 . Total popGained = 14109.0 OUT and t0 usage now 1.737 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1692 . Total popGained = 12463.0 OUT and t0 usage now 1.726 1.372\n",
      "all done exchanging OUTs for UUTs for tract 67 . Total popGained = 9110.0 OUT and t0 usage now 1.716 1.372\n",
      "all done exchanging OUTs for UUTs for tract 649 . Total popGained = 13240.0 OUT and t0 usage now 1.705 1.372\n",
      "all done exchanging OUTs for UUTs for tract 653 . Total popGained = 12467.0 OUT and t0 usage now 1.692 1.372\n",
      "all done exchanging OUTs for UUTs for tract 20 . Total popGained = 9017.0 OUT and t0 usage now 1.681 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1683 . Total popGained = 17423.0 OUT and t0 usage now 1.671 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2346 . Total popGained = 4614.0 OUT and t0 usage now 1.658 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2377 . Total popGained = 0.0 OUT and t0 usage now 1.649 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 0.0 OUT and t0 usage now 1.641 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2352 . Total popGained = 5206.0 OUT and t0 usage now 1.632 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2368 . Total popGained = 7544.0 OUT and t0 usage now 1.624 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2353 . Total popGained = 0.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1839 . Total popGained = 18694.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 672 . Total popGained = 21712.0 OUT and t0 usage now 1.592 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2308 . Total popGained = 0.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1699 . Total popGained = 24901.0 OUT and t0 usage now 1.565 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 16767.0 OUT and t0 usage now 1.553 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1700 . Total popGained = 25348.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1828 . Total popGained = 24955.0 OUT and t0 usage now 1.531 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2656 . Total popGained = 16269.0 OUT and t0 usage now 1.497 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2907 . Total popGained = 21687.0 OUT and t0 usage now 1.48 1.372\n",
      "all done exchanging OUTs for UUTs for tract 244 . Total popGained = 27963.0 OUT and t0 usage now 1.466 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2830 . Total popGained = 11265.0 OUT and t0 usage now 1.45 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1833 . Total popGained = 25926.0 OUT and t0 usage now 1.441 1.372\n",
      "all done exchanging OUTs for UUTs for tract 246 . Total popGained = 25790.0 OUT and t0 usage now 1.426 1.372\n",
      "all done exchanging OUTs for UUTs for tract 818 . Total popGained = 11483.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1239 . Total popGained = 9098.0 OUT and t0 usage now 1.402 1.372\n",
      "we have now reduced 296 usage to 1.3932176065527315 . Time for next OUT\n",
      "the most overused tract 743 with pop 769 and usage 1.9684506640529535\n",
      "all done exchanging OUTs for UUTs for tract 988 . Total popGained = 44167.0 OUT and t0 usage now 1.961 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 17706.0 OUT and t0 usage now 1.955 1.372\n",
      "all done exchanging OUTs for UUTs for tract 982 . Total popGained = 18694.0 OUT and t0 usage now 1.941 1.372\n",
      "all done exchanging OUTs for UUTs for tract 812 . Total popGained = 16370.0 OUT and t0 usage now 1.924 1.372\n",
      "all done exchanging OUTs for UUTs for tract 804 . Total popGained = 16488.0 OUT and t0 usage now 1.909 1.372\n",
      "all done exchanging OUTs for UUTs for tract 799 . Total popGained = 16748.0 OUT and t0 usage now 1.9 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 16880.0 OUT and t0 usage now 1.889 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 15872.0 OUT and t0 usage now 1.871 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 35730.0 OUT and t0 usage now 1.845 1.372\n",
      "all done exchanging OUTs for UUTs for tract 92 . Total popGained = 27621.0 OUT and t0 usage now 1.825 1.372\n",
      "all done exchanging OUTs for UUTs for tract 793 . Total popGained = 16210.0 OUT and t0 usage now 1.802 1.372\n",
      "all done exchanging OUTs for UUTs for tract 961 . Total popGained = 17122.0 OUT and t0 usage now 1.781 1.372\n",
      "all done exchanging OUTs for UUTs for tract 956 . Total popGained = 44762.0 OUT and t0 usage now 1.763 1.372\n",
      "all done exchanging OUTs for UUTs for tract 957 . Total popGained = 16062.0 OUT and t0 usage now 1.748 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 27948.0 OUT and t0 usage now 1.737 1.372\n",
      "all done exchanging OUTs for UUTs for tract 93 . Total popGained = 25103.0 OUT and t0 usage now 1.729 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2274 . Total popGained = 42571.0 OUT and t0 usage now 1.721 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2267 . Total popGained = 42523.0 OUT and t0 usage now 1.708 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2280 . Total popGained = 42584.0 OUT and t0 usage now 1.694 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2278 . Total popGained = 42266.0 OUT and t0 usage now 1.682 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2269 . Total popGained = 43531.0 OUT and t0 usage now 1.673 1.372\n",
      "all done exchanging OUTs for UUTs for tract 86 . Total popGained = 21814.0 OUT and t0 usage now 1.663 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2261 . Total popGained = 36735.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1694 . Total popGained = 15530.0 OUT and t0 usage now 1.637 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2255 . Total popGained = 40006.0 OUT and t0 usage now 1.623 1.372\n",
      "all done exchanging OUTs for UUTs for tract 89 . Total popGained = 20740.0 OUT and t0 usage now 1.611 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2258 . Total popGained = 40033.0 OUT and t0 usage now 1.598 1.372\n",
      "all done exchanging OUTs for UUTs for tract 88 . Total popGained = 40519.0 OUT and t0 usage now 1.581 1.372\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 31568.0 OUT and t0 usage now 1.571 1.372\n",
      "all done exchanging OUTs for UUTs for tract 271 . Total popGained = 34596.0 OUT and t0 usage now 1.561 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 16582.0 OUT and t0 usage now 1.551 1.372\n",
      "all done exchanging OUTs for UUTs for tract 649 . Total popGained = 19005.0 OUT and t0 usage now 1.539 1.372\n",
      "all done exchanging OUTs for UUTs for tract 655 . Total popGained = 35709.0 OUT and t0 usage now 1.53 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1832 . Total popGained = 19379.0 OUT and t0 usage now 1.514 1.372\n",
      "all done exchanging OUTs for UUTs for tract 80 . Total popGained = 38860.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1700 . Total popGained = 19816.0 OUT and t0 usage now 1.493 1.372\n",
      "all done exchanging OUTs for UUTs for tract 79 . Total popGained = 34624.0 OUT and t0 usage now 1.482 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1820 . Total popGained = 21307.0 OUT and t0 usage now 1.469 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1818 . Total popGained = 22566.0 OUT and t0 usage now 1.456 1.372\n",
      "all done exchanging OUTs for UUTs for tract 252 . Total popGained = 41527.0 OUT and t0 usage now 1.445 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1722 . Total popGained = 20606.0 OUT and t0 usage now 1.432 1.372\n",
      "all done exchanging OUTs for UUTs for tract 278 . Total popGained = 23465.0 OUT and t0 usage now 1.42 1.372\n",
      "all done exchanging OUTs for UUTs for tract 3086 . Total popGained = 34787.0 OUT and t0 usage now 1.415 1.372\n",
      "all done exchanging OUTs for UUTs for tract 159 . Total popGained = 30895.0 OUT and t0 usage now 1.408 1.372\n",
      "we have now reduced 743 usage to 1.3989463830905147 . Time for next OUT\n",
      "the most overused tract 292 with pop 1513 and usage 1.9564613002999478\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 1140.0 OUT and t0 usage now 1.945 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 1254.0 OUT and t0 usage now 1.936 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 2205.0 OUT and t0 usage now 1.927 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2260 . Total popGained = 1485.0 OUT and t0 usage now 1.918 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 1931.0 OUT and t0 usage now 1.911 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 1084.0 OUT and t0 usage now 1.896 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 3896.0 OUT and t0 usage now 1.884 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 1207.0 OUT and t0 usage now 1.864 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 2253.0 OUT and t0 usage now 1.844 1.372\n",
      "all done exchanging OUTs for UUTs for tract 782 . Total popGained = 1611.0 OUT and t0 usage now 1.821 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 1870.0 OUT and t0 usage now 1.799 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 788 . Total popGained = 1115.0 OUT and t0 usage now 1.778 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 1017.0 OUT and t0 usage now 1.76 1.372\n",
      "all done exchanging OUTs for UUTs for tract 769 . Total popGained = 2767.0 OUT and t0 usage now 1.738 1.372\n",
      "all done exchanging OUTs for UUTs for tract 674 . Total popGained = 2154.0 OUT and t0 usage now 1.725 1.372\n",
      "all done exchanging OUTs for UUTs for tract 662 . Total popGained = 1701.0 OUT and t0 usage now 1.713 1.372\n",
      "all done exchanging OUTs for UUTs for tract 675 . Total popGained = 1568.0 OUT and t0 usage now 1.705 1.372\n",
      "all done exchanging OUTs for UUTs for tract 7 . Total popGained = 2367.0 OUT and t0 usage now 1.698 1.372\n",
      "all done exchanging OUTs for UUTs for tract 648 . Total popGained = 1879.0 OUT and t0 usage now 1.685 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 1015.0 OUT and t0 usage now 1.675 1.372\n",
      "all done exchanging OUTs for UUTs for tract 652 . Total popGained = 1255.0 OUT and t0 usage now 1.662 1.372\n",
      "all done exchanging OUTs for UUTs for tract 5 . Total popGained = 2367.0 OUT and t0 usage now 1.652 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1683 . Total popGained = 1443.0 OUT and t0 usage now 1.641 1.372\n",
      "all done exchanging OUTs for UUTs for tract 92 . Total popGained = 2345.0 OUT and t0 usage now 1.628 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2321 . Total popGained = 2355.0 OUT and t0 usage now 1.616 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1686 . Total popGained = 3474.0 OUT and t0 usage now 1.607 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2386 . Total popGained = 3123.0 OUT and t0 usage now 1.595 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2288 . Total popGained = 2814.0 OUT and t0 usage now 1.587 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2303 . Total popGained = 2770.0 OUT and t0 usage now 1.577 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2348 . Total popGained = 2207.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2352 . Total popGained = 2292.0 OUT and t0 usage now 1.562 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2368 . Total popGained = 3755.0 OUT and t0 usage now 1.554 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 3118.0 OUT and t0 usage now 1.547 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2349 . Total popGained = 2184.0 OUT and t0 usage now 1.536 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1698 . Total popGained = 1287.0 OUT and t0 usage now 1.523 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1834 . Total popGained = 2098.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 98 . Total popGained = 2108.0 OUT and t0 usage now 1.5 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1837 . Total popGained = 1752.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1830 . Total popGained = 1976.0 OUT and t0 usage now 1.476 1.372\n",
      "all done exchanging OUTs for UUTs for tract 40 . Total popGained = 995.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1833 . Total popGained = 4372.0 OUT and t0 usage now 1.45 1.372\n",
      "all done exchanging OUTs for UUTs for tract 39 . Total popGained = 11064.0 OUT and t0 usage now 1.42 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2860 . Total popGained = 14002.0 OUT and t0 usage now 1.402 1.372\n",
      "we have now reduced 292 usage to 1.3984329670641824 . Time for next OUT\n",
      "the most overused tract 706 with pop 2524 and usage 1.9232660883323751\n",
      "all done exchanging OUTs for UUTs for tract 981 . Total popGained = 9816.0 OUT and t0 usage now 1.916 1.372\n",
      "all done exchanging OUTs for UUTs for tract 270 . Total popGained = 8585.0 OUT and t0 usage now 1.906 1.372\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 7566.0 OUT and t0 usage now 1.895 1.372\n",
      "all done exchanging OUTs for UUTs for tract 808 . Total popGained = 7158.0 OUT and t0 usage now 1.88 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 7084.0 OUT and t0 usage now 1.866 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 7593.0 OUT and t0 usage now 1.853 1.372\n",
      "all done exchanging OUTs for UUTs for tract 800 . Total popGained = 7240.0 OUT and t0 usage now 1.838 1.372\n",
      "all done exchanging OUTs for UUTs for tract 802 . Total popGained = 7307.0 OUT and t0 usage now 1.827 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2042 . Total popGained = 7085.0 OUT and t0 usage now 1.812 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 8843.0 OUT and t0 usage now 1.799 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 7822.0 OUT and t0 usage now 1.777 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 7323.0 OUT and t0 usage now 1.753 1.372\n",
      "all done exchanging OUTs for UUTs for tract 780 . Total popGained = 7497.0 OUT and t0 usage now 1.735 1.372\n",
      "no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\n",
      "all done exchanging OUTs for UUTs for tract 789 . Total popGained = 7370.0 OUT and t0 usage now 1.715 1.372\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 7137.0 OUT and t0 usage now 1.698 1.372\n",
      "all done exchanging OUTs for UUTs for tract 669 . Total popGained = 6821.0 OUT and t0 usage now 1.685 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2276 . Total popGained = 7664.0 OUT and t0 usage now 1.671 1.372\n",
      "all done exchanging OUTs for UUTs for tract 7 . Total popGained = 15449.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2278 . Total popGained = 6846.0 OUT and t0 usage now 1.639 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2277 . Total popGained = 7844.0 OUT and t0 usage now 1.627 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 7808.0 OUT and t0 usage now 1.616 1.372\n",
      "all done exchanging OUTs for UUTs for tract 97 . Total popGained = 7347.0 OUT and t0 usage now 1.605 1.372\n",
      "all done exchanging OUTs for UUTs for tract 93 . Total popGained = 6911.0 OUT and t0 usage now 1.597 1.372\n",
      "all done exchanging OUTs for UUTs for tract 77 . Total popGained = 7696.0 OUT and t0 usage now 1.586 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1693 . Total popGained = 7844.0 OUT and t0 usage now 1.573 1.372\n",
      "all done exchanging OUTs for UUTs for tract 25 . Total popGained = 14331.0 OUT and t0 usage now 1.567 1.372\n",
      "all done exchanging OUTs for UUTs for tract 771 . Total popGained = 5018.0 OUT and t0 usage now 1.557 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1691 . Total popGained = 7455.0 OUT and t0 usage now 1.545 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1158 . Total popGained = 7923.0 OUT and t0 usage now 1.535 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1692 . Total popGained = 6968.0 OUT and t0 usage now 1.525 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1152 . Total popGained = 6051.0 OUT and t0 usage now 1.513 1.372\n",
      "all done exchanging OUTs for UUTs for tract 663 . Total popGained = 8316.0 OUT and t0 usage now 1.501 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1801 . Total popGained = 18682.0 OUT and t0 usage now 1.492 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 12194.0 OUT and t0 usage now 1.481 1.372\n",
      "all done exchanging OUTs for UUTs for tract 69 . Total popGained = 6164.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1798 . Total popGained = 14999.0 OUT and t0 usage now 1.463 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1688 . Total popGained = 5609.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1685 . Total popGained = 7479.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 20 . Total popGained = 16685.0 OUT and t0 usage now 1.426 1.372\n",
      "all done exchanging OUTs for UUTs for tract 664 . Total popGained = 6198.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 650 . Total popGained = 5360.0 OUT and t0 usage now 1.403 1.372\n",
      "we have now reduced 706 usage to 1.3988022252709016 . Time for next OUT\n",
      "the most overused tract 709 with pop 1915 and usage 1.9038324378381053\n",
      "all done exchanging OUTs for UUTs for tract 988 . Total popGained = 4794.0 OUT and t0 usage now 1.895 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 5937.0 OUT and t0 usage now 1.885 1.372\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 4941.0 OUT and t0 usage now 1.876 1.372\n",
      "all done exchanging OUTs for UUTs for tract 984 . Total popGained = 5359.0 OUT and t0 usage now 1.862 1.372\n",
      "all done exchanging OUTs for UUTs for tract 987 . Total popGained = 5313.0 OUT and t0 usage now 1.842 1.372\n",
      "all done exchanging OUTs for UUTs for tract 800 . Total popGained = 7261.0 OUT and t0 usage now 1.831 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 6028.0 OUT and t0 usage now 1.823 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 6136.0 OUT and t0 usage now 1.805 1.372\n",
      "all done exchanging OUTs for UUTs for tract 5 . Total popGained = 5110.0 OUT and t0 usage now 1.786 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 5653.0 OUT and t0 usage now 1.764 1.372\n",
      "all done exchanging OUTs for UUTs for tract 990 . Total popGained = 5783.0 OUT and t0 usage now 1.741 1.372\n",
      "all done exchanging OUTs for UUTs for tract 955 . Total popGained = 5384.0 OUT and t0 usage now 1.722 1.372\n",
      "all done exchanging OUTs for UUTs for tract 770 . Total popGained = 7586.0 OUT and t0 usage now 1.704 1.372\n",
      "all done exchanging OUTs for UUTs for tract 956 . Total popGained = 5056.0 OUT and t0 usage now 1.686 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 5419.0 OUT and t0 usage now 1.67 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 4974.0 OUT and t0 usage now 1.659 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2274 . Total popGained = 5731.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2275 . Total popGained = 6734.0 OUT and t0 usage now 1.641 1.372\n",
      "all done exchanging OUTs for UUTs for tract 674 . Total popGained = 5447.0 OUT and t0 usage now 1.627 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2279 . Total popGained = 6244.0 OUT and t0 usage now 1.612 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2263 . Total popGained = 5235.0 OUT and t0 usage now 1.603 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1841 . Total popGained = 6040.0 OUT and t0 usage now 1.589 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 5142.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 675 . Total popGained = 4823.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2254 . Total popGained = 5936.0 OUT and t0 usage now 1.558 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1696 . Total popGained = 6835.0 OUT and t0 usage now 1.548 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1687 . Total popGained = 5372.0 OUT and t0 usage now 1.533 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1683 . Total popGained = 6191.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2256 . Total popGained = 5634.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1690 . Total popGained = 5473.0 OUT and t0 usage now 1.501 1.372\n",
      "all done exchanging OUTs for UUTs for tract 657 . Total popGained = 5906.0 OUT and t0 usage now 1.49 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1828 . Total popGained = 11145.0 OUT and t0 usage now 1.475 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1833 . Total popGained = 7033.0 OUT and t0 usage now 1.465 1.372\n",
      "all done exchanging OUTs for UUTs for tract 659 . Total popGained = 5762.0 OUT and t0 usage now 1.453 1.372\n",
      "all done exchanging OUTs for UUTs for tract 654 . Total popGained = 4980.0 OUT and t0 usage now 1.444 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1832 . Total popGained = 8665.0 OUT and t0 usage now 1.429 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1829 . Total popGained = 7640.0 OUT and t0 usage now 1.417 1.372\n",
      "all done exchanging OUTs for UUTs for tract 655 . Total popGained = 7521.0 OUT and t0 usage now 1.409 1.372\n",
      "all done exchanging OUTs for UUTs for tract 46 . Total popGained = 11143.0 OUT and t0 usage now 1.399 1.372\n",
      "we have now reduced 709 usage to 1.399311431082315 . Time for next OUT\n",
      "the most overused tract 1726 with pop 1749 and usage 1.8476897193037272\n",
      "all done exchanging OUTs for UUTs for tract 2040 . Total popGained = 24202.0 OUT and t0 usage now 1.836 1.372\n",
      "all done exchanging OUTs for UUTs for tract 982 . Total popGained = 24881.0 OUT and t0 usage now 1.822 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1802 . Total popGained = 18673.0 OUT and t0 usage now 1.816 1.372\n",
      "all done exchanging OUTs for UUTs for tract 671 . Total popGained = 24601.0 OUT and t0 usage now 1.806 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2392 . Total popGained = 18080.0 OUT and t0 usage now 1.799 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2387 . Total popGained = 24683.0 OUT and t0 usage now 1.79 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2377 . Total popGained = 25259.0 OUT and t0 usage now 1.782 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2351 . Total popGained = 24020.0 OUT and t0 usage now 1.775 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2286 . Total popGained = 25781.0 OUT and t0 usage now 1.764 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 25727.0 OUT and t0 usage now 1.755 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2363 . Total popGained = 25731.0 OUT and t0 usage now 1.749 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2345 . Total popGained = 24856.0 OUT and t0 usage now 1.742 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2358 . Total popGained = 24817.0 OUT and t0 usage now 1.733 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2354 . Total popGained = 24380.0 OUT and t0 usage now 1.725 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 26505.0 OUT and t0 usage now 1.716 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2329 . Total popGained = 26258.0 OUT and t0 usage now 1.705 1.372\n",
      "all done exchanging OUTs for UUTs for tract 22 . Total popGained = 32776.0 OUT and t0 usage now 1.7 1.372\n",
      "all done exchanging OUTs for UUTs for tract 668 . Total popGained = 24350.0 OUT and t0 usage now 1.69 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2295 . Total popGained = 18045.0 OUT and t0 usage now 1.679 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2310 . Total popGained = 17854.0 OUT and t0 usage now 1.668 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2311 . Total popGained = 23076.0 OUT and t0 usage now 1.657 1.372\n",
      "all done exchanging OUTs for UUTs for tract 102 . Total popGained = 25835.0 OUT and t0 usage now 1.649 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 24813.0 OUT and t0 usage now 1.637 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2736 . Total popGained = 5400.0 OUT and t0 usage now 1.625 1.372\n",
      "all done exchanging OUTs for UUTs for tract 892 . Total popGained = 26349.0 OUT and t0 usage now 1.609 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 24925.0 OUT and t0 usage now 1.596 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1073 . Total popGained = 27183.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 765 . Total popGained = 26409.0 OUT and t0 usage now 1.568 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1169 . Total popGained = 26684.0 OUT and t0 usage now 1.559 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1051 . Total popGained = 32439.0 OUT and t0 usage now 1.546 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1173 . Total popGained = 16917.0 OUT and t0 usage now 1.532 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2833 . Total popGained = 10147.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2784 . Total popGained = 10758.0 OUT and t0 usage now 1.5 1.372\n",
      "all done exchanging OUTs for UUTs for tract 744 . Total popGained = 34227.0 OUT and t0 usage now 1.481 1.372\n",
      "all done exchanging OUTs for UUTs for tract 88 . Total popGained = 24496.0 OUT and t0 usage now 1.468 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1052 . Total popGained = 31756.0 OUT and t0 usage now 1.454 1.372\n",
      "all done exchanging OUTs for UUTs for tract 763 . Total popGained = 22129.0 OUT and t0 usage now 1.441 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2822 . Total popGained = 9641.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 667 . Total popGained = 17489.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1157 . Total popGained = 23428.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 1726 usage to 1.398002048594716 . Time for next OUT\n",
      "the most overused tract 216 with pop 758 and usage 1.8043262967303195\n",
      "all done exchanging OUTs for UUTs for tract 2039 . Total popGained = 8666.0 OUT and t0 usage now 1.793 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2042 . Total popGained = 9039.0 OUT and t0 usage now 1.788 1.372\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 10098.0 OUT and t0 usage now 1.778 1.372\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 8865.0 OUT and t0 usage now 1.769 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 9378.0 OUT and t0 usage now 1.752 1.372\n",
      "all done exchanging OUTs for UUTs for tract 102 . Total popGained = 8324.0 OUT and t0 usage now 1.743 1.372\n",
      "all done exchanging OUTs for UUTs for tract 797 . Total popGained = 8758.0 OUT and t0 usage now 1.728 1.372\n",
      "all done exchanging OUTs for UUTs for tract 795 . Total popGained = 8986.0 OUT and t0 usage now 1.712 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 7719.0 OUT and t0 usage now 1.689 1.372\n",
      "all done exchanging OUTs for UUTs for tract 793 . Total popGained = 8827.0 OUT and t0 usage now 1.673 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 8439.0 OUT and t0 usage now 1.648 1.372\n",
      "all done exchanging OUTs for UUTs for tract 8 . Total popGained = 10988.0 OUT and t0 usage now 1.628 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 8101.0 OUT and t0 usage now 1.611 1.372\n",
      "all done exchanging OUTs for UUTs for tract 957 . Total popGained = 8165.0 OUT and t0 usage now 1.587 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1933 . Total popGained = 9794.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 7848.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1692 . Total popGained = 8684.0 OUT and t0 usage now 1.555 1.372\n",
      "all done exchanging OUTs for UUTs for tract 652 . Total popGained = 8713.0 OUT and t0 usage now 1.542 1.372\n",
      "all done exchanging OUTs for UUTs for tract 658 . Total popGained = 8061.0 OUT and t0 usage now 1.53 1.372\n",
      "all done exchanging OUTs for UUTs for tract 651 . Total popGained = 8879.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1840 . Total popGained = 10579.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1841 . Total popGained = 9323.0 OUT and t0 usage now 1.498 1.372\n",
      "all done exchanging OUTs for UUTs for tract 772 . Total popGained = 8071.0 OUT and t0 usage now 1.488 1.372\n",
      "all done exchanging OUTs for UUTs for tract 92 . Total popGained = 9822.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 252 . Total popGained = 7520.0 OUT and t0 usage now 1.46 1.372\n",
      "all done exchanging OUTs for UUTs for tract 251 . Total popGained = 8291.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 8624.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 93 . Total popGained = 8294.0 OUT and t0 usage now 1.43 1.372\n",
      "all done exchanging OUTs for UUTs for tract 766 . Total popGained = 5093.0 OUT and t0 usage now 1.418 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2778 . Total popGained = 3693.0 OUT and t0 usage now 1.404 1.372\n",
      "we have now reduced 216 usage to 1.3958585409668716 . Time for next OUT\n",
      "the most overused tract 227 with pop 318 and usage 1.6954937497770062\n",
      "all done exchanging OUTs for UUTs for tract 137 . Total popGained = 11431.0 OUT and t0 usage now 1.69 1.372\n",
      "all done exchanging OUTs for UUTs for tract 140 . Total popGained = 17220.0 OUT and t0 usage now 1.687 1.372\n",
      "all done exchanging OUTs for UUTs for tract 695 . Total popGained = 16049.0 OUT and t0 usage now 1.683 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1040 . Total popGained = 9354.0 OUT and t0 usage now 1.672 1.372\n",
      "all done exchanging OUTs for UUTs for tract 56 . Total popGained = 16199.0 OUT and t0 usage now 1.663 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1237 . Total popGained = 8509.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 690 . Total popGained = 17755.0 OUT and t0 usage now 1.642 1.372\n",
      "all done exchanging OUTs for UUTs for tract 694 . Total popGained = 19438.0 OUT and t0 usage now 1.636 1.372\n",
      "all done exchanging OUTs for UUTs for tract 922 . Total popGained = 10626.0 OUT and t0 usage now 1.62 1.372\n",
      "all done exchanging OUTs for UUTs for tract 699 . Total popGained = 16583.0 OUT and t0 usage now 1.608 1.372\n",
      "all done exchanging OUTs for UUTs for tract 734 . Total popGained = 18317.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 4576.0 OUT and t0 usage now 1.593 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2310 . Total popGained = 7420.0 OUT and t0 usage now 1.58 1.372\n",
      "all done exchanging OUTs for UUTs for tract 840 . Total popGained = 16375.0 OUT and t0 usage now 1.562 1.372\n",
      "all done exchanging OUTs for UUTs for tract 28 . Total popGained = 8261.0 OUT and t0 usage now 1.554 1.372\n",
      "all done exchanging OUTs for UUTs for tract 902 . Total popGained = 8208.0 OUT and t0 usage now 1.543 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2307 . Total popGained = 5720.0 OUT and t0 usage now 1.532 1.372\n",
      "all done exchanging OUTs for UUTs for tract 881 . Total popGained = 10385.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 869 . Total popGained = 11536.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2390 . Total popGained = 3735.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2334 . Total popGained = 5239.0 OUT and t0 usage now 1.495 1.372\n",
      "all done exchanging OUTs for UUTs for tract 950 . Total popGained = 10155.0 OUT and t0 usage now 1.485 1.372\n",
      "all done exchanging OUTs for UUTs for tract 872 . Total popGained = 9416.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 844 . Total popGained = 15992.0 OUT and t0 usage now 1.463 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2359 . Total popGained = 4992.0 OUT and t0 usage now 1.456 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2296 . Total popGained = 5687.0 OUT and t0 usage now 1.443 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2364 . Total popGained = 3735.0 OUT and t0 usage now 1.43 1.372\n",
      "all done exchanging OUTs for UUTs for tract 880 . Total popGained = 15244.0 OUT and t0 usage now 1.422 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2376 . Total popGained = 13273.0 OUT and t0 usage now 1.404 1.372\n",
      "we have now reduced 227 usage to 1.398906838821596 . Time for next OUT\n",
      "the most overused tract 181 with pop 1389 and usage 1.6926047947646872\n",
      "all done exchanging OUTs for UUTs for tract 279 . Total popGained = 7104.0 OUT and t0 usage now 1.688 1.372\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 6934.0 OUT and t0 usage now 1.677 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2040 . Total popGained = 4327.0 OUT and t0 usage now 1.669 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 4789.0 OUT and t0 usage now 1.66 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1798 . Total popGained = 4056.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2041 . Total popGained = 3728.0 OUT and t0 usage now 1.639 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2326 . Total popGained = 5206.0 OUT and t0 usage now 1.629 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1805 . Total popGained = 6066.0 OUT and t0 usage now 1.622 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2387 . Total popGained = 8521.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 103 . Total popGained = 5469.0 OUT and t0 usage now 1.608 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 5721.0 OUT and t0 usage now 1.601 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2270 . Total popGained = 4988.0 OUT and t0 usage now 1.592 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2357 . Total popGained = 5552.0 OUT and t0 usage now 1.582 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2363 . Total popGained = 8187.0 OUT and t0 usage now 1.573 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2379 . Total popGained = 6814.0 OUT and t0 usage now 1.565 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2330 . Total popGained = 5206.0 OUT and t0 usage now 1.557 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2349 . Total popGained = 5372.0 OUT and t0 usage now 1.549 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2338 . Total popGained = 6233.0 OUT and t0 usage now 1.539 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 5832.0 OUT and t0 usage now 1.528 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2307 . Total popGained = 5866.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2389 . Total popGained = 6337.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2388 . Total popGained = 6602.0 OUT and t0 usage now 1.503 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2371 . Total popGained = 7324.0 OUT and t0 usage now 1.492 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2296 . Total popGained = 5807.0 OUT and t0 usage now 1.484 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2315 . Total popGained = 6895.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2316 . Total popGained = 5347.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1188 . Total popGained = 5420.0 OUT and t0 usage now 1.452 1.372\n",
      "all done exchanging OUTs for UUTs for tract 30 . Total popGained = 5303.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 24 . Total popGained = 6354.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1189 . Total popGained = 5553.0 OUT and t0 usage now 1.416 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1045 . Total popGained = 6003.0 OUT and t0 usage now 1.402 1.372\n",
      "we have now reduced 181 usage to 1.3989456353397476 . Time for next OUT\n",
      "the most overused tract 194 with pop 795 and usage 1.671409425544806\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 14293.0 OUT and t0 usage now 1.66 1.372\n",
      "all done exchanging OUTs for UUTs for tract 671 . Total popGained = 11374.0 OUT and t0 usage now 1.65 1.372\n",
      "all done exchanging OUTs for UUTs for tract 9 . Total popGained = 10539.0 OUT and t0 usage now 1.635 1.372\n",
      "all done exchanging OUTs for UUTs for tract 989 . Total popGained = 14100.0 OUT and t0 usage now 1.625 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2281 . Total popGained = 9427.0 OUT and t0 usage now 1.617 1.372\n",
      "all done exchanging OUTs for UUTs for tract 811 . Total popGained = 11063.0 OUT and t0 usage now 1.602 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2267 . Total popGained = 11318.0 OUT and t0 usage now 1.591 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2275 . Total popGained = 15915.0 OUT and t0 usage now 1.578 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2266 . Total popGained = 9040.0 OUT and t0 usage now 1.568 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2264 . Total popGained = 13815.0 OUT and t0 usage now 1.552 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2282 . Total popGained = 5193.0 OUT and t0 usage now 1.534 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2263 . Total popGained = 16604.0 OUT and t0 usage now 1.524 1.372\n",
      "all done exchanging OUTs for UUTs for tract 792 . Total popGained = 11244.0 OUT and t0 usage now 1.504 1.372\n",
      "all done exchanging OUTs for UUTs for tract 784 . Total popGained = 11151.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2257 . Total popGained = 14285.0 OUT and t0 usage now 1.46 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2255 . Total popGained = 7929.0 OUT and t0 usage now 1.438 1.372\n",
      "all done exchanging OUTs for UUTs for tract 960 . Total popGained = 14775.0 OUT and t0 usage now 1.42 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2256 . Total popGained = 5298.0 OUT and t0 usage now 1.4 1.372\n",
      "we have now reduced 194 usage to 1.399526197522081 . Time for next OUT\n",
      "the most overused tract 108 with pop 326 and usage 1.671352954254219\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 8587.0 OUT and t0 usage now 1.658 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 8427.0 OUT and t0 usage now 1.651 1.372\n",
      "all done exchanging OUTs for UUTs for tract 981 . Total popGained = 10140.0 OUT and t0 usage now 1.635 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1838 . Total popGained = 7568.0 OUT and t0 usage now 1.626 1.372\n",
      "all done exchanging OUTs for UUTs for tract 809 . Total popGained = 10035.0 OUT and t0 usage now 1.615 1.372\n",
      "all done exchanging OUTs for UUTs for tract 807 . Total popGained = 5788.0 OUT and t0 usage now 1.6 1.372\n",
      "all done exchanging OUTs for UUTs for tract 785 . Total popGained = 4812.0 OUT and t0 usage now 1.589 1.372\n",
      "all done exchanging OUTs for UUTs for tract 787 . Total popGained = 4964.0 OUT and t0 usage now 1.569 1.372\n",
      "all done exchanging OUTs for UUTs for tract 791 . Total popGained = 8445.0 OUT and t0 usage now 1.546 1.372\n",
      "all done exchanging OUTs for UUTs for tract 780 . Total popGained = 7816.0 OUT and t0 usage now 1.521 1.372\n",
      "all done exchanging OUTs for UUTs for tract 955 . Total popGained = 6299.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 960 . Total popGained = 4821.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 794 . Total popGained = 4990.0 OUT and t0 usage now 1.462 1.372\n",
      "all done exchanging OUTs for UUTs for tract 669 . Total popGained = 7337.0 OUT and t0 usage now 1.454 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1835 . Total popGained = 1264.0 OUT and t0 usage now 1.437 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1933 . Total popGained = 4391.0 OUT and t0 usage now 1.423 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1831 . Total popGained = 7076.0 OUT and t0 usage now 1.406 1.372\n",
      "we have now reduced 108 usage to 1.398091329889257 . Time for next OUT\n",
      "the most overused tract 45 with pop 1200 and usage 1.6647223217132447\n",
      "all done exchanging OUTs for UUTs for tract 2041 . Total popGained = 13609.0 OUT and t0 usage now 1.659 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2042 . Total popGained = 3900.0 OUT and t0 usage now 1.653 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2387 . Total popGained = 5453.0 OUT and t0 usage now 1.645 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2337 . Total popGained = 4423.0 OUT and t0 usage now 1.637 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 4536.0 OUT and t0 usage now 1.629 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2364 . Total popGained = 5059.0 OUT and t0 usage now 1.621 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2334 . Total popGained = 5914.0 OUT and t0 usage now 1.614 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2369 . Total popGained = 12498.0 OUT and t0 usage now 1.607 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2367 . Total popGained = 10503.0 OUT and t0 usage now 1.599 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 5446.0 OUT and t0 usage now 1.591 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2349 . Total popGained = 9088.0 OUT and t0 usage now 1.581 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2328 . Total popGained = 2349.0 OUT and t0 usage now 1.576 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2295 . Total popGained = 0.0 OUT and t0 usage now 1.565 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 12808.0 OUT and t0 usage now 1.555 1.372\n",
      "all done exchanging OUTs for UUTs for tract 29 . Total popGained = 1821.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 13335.0 OUT and t0 usage now 1.537 1.372\n",
      "all done exchanging OUTs for UUTs for tract 69 . Total popGained = 12626.0 OUT and t0 usage now 1.525 1.372\n",
      "all done exchanging OUTs for UUTs for tract 68 . Total popGained = 11694.0 OUT and t0 usage now 1.515 1.372\n",
      "all done exchanging OUTs for UUTs for tract 662 . Total popGained = 13171.0 OUT and t0 usage now 1.499 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 11575.0 OUT and t0 usage now 1.484 1.372\n",
      "all done exchanging OUTs for UUTs for tract 9 . Total popGained = 14520.0 OUT and t0 usage now 1.473 1.372\n",
      "all done exchanging OUTs for UUTs for tract 792 . Total popGained = 13785.0 OUT and t0 usage now 1.45 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 12060.0 OUT and t0 usage now 1.429 1.372\n",
      "all done exchanging OUTs for UUTs for tract 940 . Total popGained = 6742.0 OUT and t0 usage now 1.411 1.372\n",
      "we have now reduced 45 usage to 1.3952135546248492 . Time for next OUT\n",
      "the most overused tract 3099 with pop 349 and usage 1.6240984572495274\n",
      "all done exchanging OUTs for UUTs for tract 191 . Total popGained = 6634.0 OUT and t0 usage now 1.618 1.372\n",
      "all done exchanging OUTs for UUTs for tract 204 . Total popGained = 7321.0 OUT and t0 usage now 1.613 1.372\n",
      "all done exchanging OUTs for UUTs for tract 3105 . Total popGained = 5138.0 OUT and t0 usage now 1.607 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1799 . Total popGained = 7268.0 OUT and t0 usage now 1.603 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1147 . Total popGained = 8375.0 OUT and t0 usage now 1.598 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1171 . Total popGained = 11115.0 OUT and t0 usage now 1.587 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1187 . Total popGained = 11724.0 OUT and t0 usage now 1.575 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1241 . Total popGained = 10461.0 OUT and t0 usage now 1.567 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1169 . Total popGained = 7138.0 OUT and t0 usage now 1.559 1.372\n",
      "all done exchanging OUTs for UUTs for tract 18 . Total popGained = 8708.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 898 . Total popGained = 5804.0 OUT and t0 usage now 1.536 1.372\n",
      "all done exchanging OUTs for UUTs for tract 820 . Total popGained = 11384.0 OUT and t0 usage now 1.519 1.372\n",
      "all done exchanging OUTs for UUTs for tract 908 . Total popGained = 7374.0 OUT and t0 usage now 1.507 1.372\n",
      "all done exchanging OUTs for UUTs for tract 830 . Total popGained = 2095.0 OUT and t0 usage now 1.496 1.372\n",
      "all done exchanging OUTs for UUTs for tract 935 . Total popGained = 9276.0 OUT and t0 usage now 1.482 1.372\n",
      "all done exchanging OUTs for UUTs for tract 872 . Total popGained = 8126.0 OUT and t0 usage now 1.47 1.372\n",
      "all done exchanging OUTs for UUTs for tract 943 . Total popGained = 7261.0 OUT and t0 usage now 1.46 1.372\n",
      "all done exchanging OUTs for UUTs for tract 880 . Total popGained = 8267.0 OUT and t0 usage now 1.446 1.372\n",
      "all done exchanging OUTs for UUTs for tract 882 . Total popGained = 11853.0 OUT and t0 usage now 1.427 1.372\n",
      "all done exchanging OUTs for UUTs for tract 883 . Total popGained = 9714.0 OUT and t0 usage now 1.409 1.372\n",
      "we have now reduced 3099 usage to 1.3975960707440649 . Time for next OUT\n",
      "the most overused tract 184 with pop 3043 and usage 1.60521592403614\n",
      "all done exchanging OUTs for UUTs for tract 279 . Total popGained = 11759.0 OUT and t0 usage now 1.6 1.372\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 9988.0 OUT and t0 usage now 1.59 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1802 . Total popGained = 8764.0 OUT and t0 usage now 1.584 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1798 . Total popGained = 9303.0 OUT and t0 usage now 1.575 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2326 . Total popGained = 10560.0 OUT and t0 usage now 1.564 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2347 . Total popGained = 10404.0 OUT and t0 usage now 1.559 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2386 . Total popGained = 7196.0 OUT and t0 usage now 1.55 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2370 . Total popGained = 6830.0 OUT and t0 usage now 1.543 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2373 . Total popGained = 6583.0 OUT and t0 usage now 1.534 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2365 . Total popGained = 6886.0 OUT and t0 usage now 1.527 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2375 . Total popGained = 9945.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2345 . Total popGained = 6385.0 OUT and t0 usage now 1.51 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2379 . Total popGained = 5954.0 OUT and t0 usage now 1.502 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2380 . Total popGained = 10474.0 OUT and t0 usage now 1.494 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2335 . Total popGained = 6669.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2333 . Total popGained = 7251.0 OUT and t0 usage now 1.476 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2356 . Total popGained = 8045.0 OUT and t0 usage now 1.467 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2355 . Total popGained = 6605.0 OUT and t0 usage now 1.457 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2285 . Total popGained = 11173.0 OUT and t0 usage now 1.445 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1799 . Total popGained = 9877.0 OUT and t0 usage now 1.442 1.372\n",
      "all done exchanging OUTs for UUTs for tract 984 . Total popGained = 5092.0 OUT and t0 usage now 1.43 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2298 . Total popGained = 7298.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1239 . Total popGained = 7848.0 OUT and t0 usage now 1.406 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2295 . Total popGained = 4464.0 OUT and t0 usage now 1.401 1.372\n",
      "we have now reduced 184 usage to 1.3978734751375617 . Time for next OUT\n",
      "the most overused tract 994 with pop 658 and usage 1.5556752551304076\n",
      "all done exchanging OUTs for UUTs for tract 985 . Total popGained = 6932.0 OUT and t0 usage now 1.544 1.372\n",
      "all done exchanging OUTs for UUTs for tract 90 . Total popGained = 8012.0 OUT and t0 usage now 1.536 1.372\n",
      "all done exchanging OUTs for UUTs for tract 85 . Total popGained = 6908.0 OUT and t0 usage now 1.529 1.372\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 6535.0 OUT and t0 usage now 1.518 1.372\n",
      "all done exchanging OUTs for UUTs for tract 670 . Total popGained = 5848.0 OUT and t0 usage now 1.509 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 8679.0 OUT and t0 usage now 1.495 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 7447.0 OUT and t0 usage now 1.483 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 7102.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 792 . Total popGained = 6225.0 OUT and t0 usage now 1.442 1.372\n",
      "all done exchanging OUTs for UUTs for tract 784 . Total popGained = 7248.0 OUT and t0 usage now 1.416 1.372\n",
      "all done exchanging OUTs for UUTs for tract 86 . Total popGained = 10536.0 OUT and t0 usage now 1.4 1.372\n",
      "we have now reduced 994 usage to 1.3959749305213256 . Time for next OUT\n",
      "the most overused tract 241 with pop 942 and usage 1.5277985712489393\n",
      "all done exchanging OUTs for UUTs for tract 281 . Total popGained = 3435.0 OUT and t0 usage now 1.522 1.372\n",
      "all done exchanging OUTs for UUTs for tract 274 . Total popGained = 3435.0 OUT and t0 usage now 1.511 1.372\n",
      "all done exchanging OUTs for UUTs for tract 273 . Total popGained = 7997.0 OUT and t0 usage now 1.506 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1801 . Total popGained = 2979.0 OUT and t0 usage now 1.497 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1150 . Total popGained = 8737.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1167 . Total popGained = 5421.0 OUT and t0 usage now 1.476 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1045 . Total popGained = 5851.0 OUT and t0 usage now 1.464 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1042 . Total popGained = 7704.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1256 . Total popGained = 6737.0 OUT and t0 usage now 1.435 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1209 . Total popGained = 9143.0 OUT and t0 usage now 1.424 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1196 . Total popGained = 7149.0 OUT and t0 usage now 1.413 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1092 . Total popGained = 9191.0 OUT and t0 usage now 1.403 1.372\n",
      "we have now reduced 241 usage to 1.3996093329181176 . Time for next OUT\n",
      "the most overused tract 1131 with pop 1686 and usage 1.5032930276768115\n",
      "all done exchanging OUTs for UUTs for tract 2039 . Total popGained = 13894.0 OUT and t0 usage now 1.49 1.372\n",
      "all done exchanging OUTs for UUTs for tract 668 . Total popGained = 11319.0 OUT and t0 usage now 1.487 1.372\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 14567.0 OUT and t0 usage now 1.479 1.372\n",
      "all done exchanging OUTs for UUTs for tract 774 . Total popGained = 13966.0 OUT and t0 usage now 1.466 1.372\n",
      "all done exchanging OUTs for UUTs for tract 775 . Total popGained = 12731.0 OUT and t0 usage now 1.451 1.372\n",
      "all done exchanging OUTs for UUTs for tract 803 . Total popGained = 12426.0 OUT and t0 usage now 1.439 1.372\n",
      "all done exchanging OUTs for UUTs for tract 798 . Total popGained = 13558.0 OUT and t0 usage now 1.423 1.372\n",
      "all done exchanging OUTs for UUTs for tract 805 . Total popGained = 12644.0 OUT and t0 usage now 1.401 1.372\n",
      "we have now reduced 1131 usage to 1.3967065220064578 . Time for next OUT\n",
      "the most overused tract 2544 with pop 1721 and usage 1.4919600203105936\n",
      "all done exchanging OUTs for UUTs for tract 984 . Total popGained = 7810.0 OUT and t0 usage now 1.477 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2272 . Total popGained = 10624.0 OUT and t0 usage now 1.467 1.372\n",
      "all done exchanging OUTs for UUTs for tract 9 . Total popGained = 3301.0 OUT and t0 usage now 1.453 1.372\n",
      "all done exchanging OUTs for UUTs for tract 103 . Total popGained = 7922.0 OUT and t0 usage now 1.443 1.372\n",
      "all done exchanging OUTs for UUTs for tract 778 . Total popGained = 5871.0 OUT and t0 usage now 1.435 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2267 . Total popGained = 9141.0 OUT and t0 usage now 1.423 1.372\n",
      "all done exchanging OUTs for UUTs for tract 804 . Total popGained = 4783.0 OUT and t0 usage now 1.411 1.372\n",
      "we have now reduced 2544 usage to 1.3978174978371911 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.4750269930239013\n",
      "all done exchanging OUTs for UUTs for tract 24 . Total popGained = 4170.0 OUT and t0 usage now 1.47 1.372\n",
      "all done exchanging OUTs for UUTs for tract 26 . Total popGained = 4667.0 OUT and t0 usage now 1.466 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2285 . Total popGained = 9737.0 OUT and t0 usage now 1.457 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2312 . Total popGained = 9038.0 OUT and t0 usage now 1.447 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2330 . Total popGained = 7659.0 OUT and t0 usage now 1.438 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2316 . Total popGained = 7323.0 OUT and t0 usage now 1.428 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2337 . Total popGained = 6669.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2336 . Total popGained = 6608.0 OUT and t0 usage now 1.409 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2382 . Total popGained = 10789.0 OUT and t0 usage now 1.401 1.372\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 247 with pop 284 and usage 1.4576553129097967\n",
      "all done exchanging OUTs for UUTs for tract 276 . Total popGained = 11164.0 OUT and t0 usage now 1.452 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1191 . Total popGained = 4557.0 OUT and t0 usage now 1.44 1.372\n",
      "all done exchanging OUTs for UUTs for tract 275 . Total popGained = 9347.0 OUT and t0 usage now 1.431 1.372\n",
      "all done exchanging OUTs for UUTs for tract 1066 . Total popGained = 13174.0 OUT and t0 usage now 1.419 1.372\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 10755.0 OUT and t0 usage now 1.411 1.372\n",
      "all done exchanging OUTs for UUTs for tract 2762 . Total popGained = 9996.0 OUT and t0 usage now 1.407 1.372\n",
      "we have now reduced 247 usage to 1.3991543203078076 . Time for next OUT\n",
      "the most overused tract 3095 with pop 483 and usage 1.4561801987420921\n",
      "all done exchanging OUTs for UUTs for tract 191 . Total popGained = 5162.0 OUT and t0 usage now 1.449 1.367\n",
      "all done exchanging OUTs for UUTs for tract 202 . Total popGained = 7742.0 OUT and t0 usage now 1.444 1.363\n",
      "all done exchanging OUTs for UUTs for tract 131 . Total popGained = 5452.0 OUT and t0 usage now 1.43 1.349\n",
      "all done exchanging OUTs for UUTs for tract 3105 . Total popGained = 4510.0 OUT and t0 usage now 1.424 1.343\n",
      "all done exchanging OUTs for UUTs for tract 1795 . Total popGained = 3509.0 OUT and t0 usage now 1.42 1.339\n",
      "all done exchanging OUTs for UUTs for tract 281 . Total popGained = 5787.0 OUT and t0 usage now 1.415 1.334\n",
      "all done exchanging OUTs for UUTs for tract 1155 . Total popGained = 4153.0 OUT and t0 usage now 1.41 1.329\n",
      "all done exchanging OUTs for UUTs for tract 280 . Total popGained = 4220.0 OUT and t0 usage now 1.403 1.322\n",
      "we have now reduced 3095 usage to 1.398825089015493 . Time for next OUT\n",
      "the most overused tract 1727 with pop 2308 and usage 1.4473808375671393\n",
      "all done exchanging OUTs for UUTs for tract 279 . Total popGained = 17837.0 OUT and t0 usage now 1.442 1.318\n",
      "all done exchanging OUTs for UUTs for tract 272 . Total popGained = 16988.0 OUT and t0 usage now 1.432 1.318\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 20685.0 OUT and t0 usage now 1.427 1.318\n",
      "all done exchanging OUTs for UUTs for tract 2317 . Total popGained = 23328.0 OUT and t0 usage now 1.42 1.318\n",
      "all done exchanging OUTs for UUTs for tract 2391 . Total popGained = 5107.0 OUT and t0 usage now 1.413 1.318\n",
      "all done exchanging OUTs for UUTs for tract 2381 . Total popGained = 17314.0 OUT and t0 usage now 1.405 1.318\n",
      "we have now reduced 1727 usage to 1.399727628558172 . Time for next OUT\n",
      "the most overused tract 53 with pop 500 and usage 1.4392773871376385\n",
      "all done exchanging OUTs for UUTs for tract 671 . Total popGained = 3080.0 OUT and t0 usage now 1.436 1.318\n",
      "all done exchanging OUTs for UUTs for tract 779 . Total popGained = 3418.0 OUT and t0 usage now 1.431 1.318\n",
      "all done exchanging OUTs for UUTs for tract 777 . Total popGained = 3106.0 OUT and t0 usage now 1.42 1.318\n",
      "all done exchanging OUTs for UUTs for tract 810 . Total popGained = 2692.0 OUT and t0 usage now 1.41 1.318\n",
      "we have now reduced 53 usage to 1.3995746931697697 . Time for next OUT\n",
      "the most overused tract 1732 with pop 4666 and usage 1.4367101940372193\n",
      "all done exchanging OUTs for UUTs for tract 796 . Total popGained = 16769.0 OUT and t0 usage now 1.421 1.318\n",
      "all done exchanging OUTs for UUTs for tract 753 . Total popGained = 17857.0 OUT and t0 usage now 1.395 1.318\n",
      "we have now reduced 1732 usage to 1.3948401323654054 . Time for next OUT\n",
      "the most overused tract 231 with pop 653 and usage 1.4339844335898855\n",
      "all done exchanging OUTs for UUTs for tract 3104 . Total popGained = 0.0 OUT and t0 usage now 1.429 1.318\n",
      "all done exchanging OUTs for UUTs for tract 1800 . Total popGained = 0.0 OUT and t0 usage now 1.426 1.318\n",
      "all done exchanging OUTs for UUTs for tract 882 . Total popGained = 1719.0 OUT and t0 usage now 1.414 1.318\n",
      "all done exchanging OUTs for UUTs for tract 938 . Total popGained = 1748.0 OUT and t0 usage now 1.398 1.318\n",
      "we have now reduced 231 usage to 1.3978953581698006 . Time for next OUT\n",
      "the most overused tract 35 with pop 165 and usage 1.4300770699761023\n",
      "all done exchanging OUTs for UUTs for tract 660 . Total popGained = 20671.0 OUT and t0 usage now 1.411 1.318\n",
      "we have now reduced 35 usage to 1.3958100453191828 . Time for next OUT\n",
      "the most overused tract 219 with pop 1015 and usage 1.4261391192944435\n",
      "all done exchanging OUTs for UUTs for tract 854 . Total popGained = 10170.0 OUT and t0 usage now 1.411 1.318\n",
      "all done exchanging OUTs for UUTs for tract 240 . Total popGained = 25737.0 OUT and t0 usage now 1.398 1.318\n",
      "we have now reduced 219 usage to 1.3978509152025884 . Time for next OUT\n",
      "the most overused tract 4 with pop 89 and usage 1.4203945675175458\n",
      "all done exchanging OUTs for UUTs for tract 164 . Total popGained = 1523.0 OUT and t0 usage now 1.412 1.318\n",
      "we have now reduced 4 usage to 1.399476316284457 . Time for next OUT\n",
      "the most overused tract 207 with pop 686 and usage 1.41201174841705\n",
      "all done exchanging OUTs for UUTs for tract 776 . Total popGained = 13994.0 OUT and t0 usage now 1.4 1.318\n",
      "we have now reduced 207 usage to 1.3996218032275238 . Time for next OUT\n",
      "the most overused tract 999 with pop 843 and usage 1.4096664611728118\n",
      "we have now reduced 999 usage to 1.398939223904045 . Time for next OUT\n",
      "the most overused tract 122 with pop 842 and usage 1.40957311013327\n",
      "we have now reduced 122 usage to 1.3992199821466744 . Time for next OUT\n",
      "the most overused tract 52 with pop 1197 and usage 1.4073256832603773\n",
      "we have now reduced 52 usage to 1.3965984459916105 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n",
      "we have now reduced 688 usage to 1.3999917557398929 . Time for next OUT\n",
      "the most overused tract 688 with pop 984 and usage 1.3999917557398929\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_53/2507848697.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     27\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mtt\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mHDtractList\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m                     \u001b[0misOUTboundaryTract\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 29\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mtractGeom\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mOUT\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mMAPboundary\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     30\u001b[0m                 \u001b[0misOUTboundaryTract\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     31\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misOUTboundaryTract\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"yes\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/geopandas/geoseries.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    606\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrapped_pandas_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"__getitem__\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mdoc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/geopandas/geoseries.py\u001b[0m in \u001b[0;36m_wrapped_pandas_method\u001b[0;34m(self, mtd, *args, **kwargs)\u001b[0m\n\u001b[1;32m    599\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_wrapped_pandas_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmtd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m         \u001b[0;34m\"\"\"Wrap a generic pandas method to ensure it returns a GeoSeries\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 601\u001b[0;31m         \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmtd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    602\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    603\u001b[0m             \u001b[0mval\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mGeoSeries\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.9/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    936\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munpack_1tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    937\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 938\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_should_fallback_to_positional\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    939\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    940\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "print(\"This code block aims to narrow the breadth of the precinct usage distribution\")  #based on OH_patch_legisl_org patching block\n",
    "# GENERAL SCHEME - identify the most overused tract (OUT) and a list of underused tract (UUT's)\n",
    "#  Loop over all tracts to find HD's that use this tract\n",
    "#     Among these HDs, determine which ones have this OUT on its boundary (OUT intersects MAPboundary or has nonHD neighbors)\n",
    "#       For each tract w this OUT as a boundary tract, ID which ones contain a neighbor of a non-HD UUT (an addable UUT)\n",
    "#          Score all qualifying HDs based on the centrality of its most proximate qualifying UUT\n",
    "#            For best-scoring HD, trade out OUT for UUT's until HD3pop recovered\n",
    "maxUUTuse = 0.8 #we'll prioritize subbing in UnderUsedMunis which are this much underused\n",
    "A0 = 1.000   #scorecard constant for encouraging underused munis vs. proximate munis\n",
    "A1 = 4.000   #penalty for using non-UnderUsedMuni when no real UUM available\n",
    "nLoopPrint = 10    \n",
    "maxOUTs = nWayOverusedTracts #20 #arbitrary number of OUTs to depress (debug)\n",
    "nOUTs = 0\n",
    "OUTusageStopLoop = 1.4 #when we get the OUT's usage below this, move on to another OUM\n",
    "while nOUTs <= maxOUTs and np.max(tractUse) > OUTusageStopLoop:\n",
    "\n",
    "    nOUTs +=1  #working on a new OUM\n",
    "    OUT = tractUse.index(np.max(tractUse))\n",
    "    print(\"the most overused tract\",OUT,\"with pop\",tractPop[OUT],\"and usage\",tractUse[OUT])\n",
    "    OUT_centerDistance = [0.]*nTracts  #we'll score HDs based on how far the OUT is from the HD centerpoint.  Fake as 0 if nonqualifying\n",
    "    sortedOUTCD = [0.]*nTracts\n",
    "    nQualifyingTracts = 0\n",
    "    for t in range(nTracts):\n",
    "        if OUT in HDtractList[t] and t != OUT:\n",
    "            isOUTboundaryTract = \"no\"\n",
    "            for tt in neighborList[OUT]:\n",
    "                if tt not in HDtractList[t]:\n",
    "                    isOUTboundaryTract = \"yes\"\n",
    "            if tractGeom[OUT].intersects(MAPboundary):\n",
    "                isOUTboundaryTract = \"yes\"\n",
    "            if isOUTboundaryTract == \"yes\":\n",
    "                OUT_centerDistance[t] = -1.*tractCP[t].distance(tractCP[OUT])  # -1 so low score is preferred (farthest away)\n",
    "                sortedOUTCD[t] = OUT_centerDistance[t]\n",
    "                nQualifyingTracts +=1\n",
    "    sortedOUTCD.sort()\n",
    "    T = 0\n",
    "    while T < nQualifyingTracts and tractUse[OUT] > OUTusageStopLoop:  \n",
    "        T += 1\n",
    "        t = OUT_centerDistance.index(sortedOUTCD[T])  #start with HD that is farthest from the OUT and work in\n",
    "        if OUT in HDtractList[t]:  #should be always true, but just in case\n",
    "            addScore = [0.]*nTracts  #underused tracts closer to HD tractCP will be assigned a more negative score\n",
    "            for tt in range(nTracts):\n",
    "                if tt not in HDtractList[t] and tractUse[tt] < 1.0:  #we'll check adjacency later.  For now, score all under-used tracts\n",
    "                    addScore[tt] = (tractUse[tt] - 1.) * abs(OUT_centerDistance[t]) / tractCP[t].distance(tractCP[tt]) \n",
    "        \n",
    "            OUTgroup = [OUT]  #include 1- and 2-level overUsed neighbors of the OUT to get rid of here\n",
    "            for tt in neighborList[OUT]:\n",
    "                if tt in HDtractList[t] and tractUse[tt] > 1.0 and tt not in OUTgroup and tt != t:  \n",
    "                    OUTgroup.append(tt)\n",
    "                for ttt in neighborList[tt]:  #now look at 2nd-level neighbors.  Use a higher tractUse bar to include in group\n",
    "                    if ttt in HDtractList[t] and tractUse[ttt] > 1.2 and ttt not in OUTgroup and ttt != t:\n",
    "                        OUTgroup.append(ttt)\n",
    "            popToShed = 0.\n",
    "            for tt in OUTgroup:\n",
    "                popToShed += tractPop[tt]*getFrac(tt,partialTractNo[t],partialTractUse[t])\n",
    "                HDtractList[t].remove(tt)\n",
    "                tractUse[tt] -= tractPop[t] / avgDistrictPop * getFrac(tt,partialTractNo[t],partialTractUse[t])\n",
    "            popGained = 0.\n",
    "            #print(\"We are shedding tracts\",OUTgroup,\"with total pop in HD\",popToShed,\"led by\",OUT,\"from Home District\",t)\n",
    "            while popToShed > 0.6*np.average(tractPop):  #get close enough; we can square up later\n",
    "                addableUUTlist = []\n",
    "                addableUUTscore = [0.]*nTracts\n",
    "                for tt in HDtractList[t]:  #OUT and friends already removed from this list, so their neighbors not checked\n",
    "                    for n in neighborList[tt]:\n",
    "                        if tt not in OUTgroup and n not in addableUUTlist and n not in HDtractList[t] and tractUse[n] < 1.0 :\n",
    "                            addableUUTlist.append(n)\n",
    "                            addableUUTscore[n] = addScore[n]\n",
    "                if addableUUTlist == []:  #no neighboring underUsed tracts.  relax underUse constraint\n",
    "                    print(\"no nearby truly underused tracts.  We will allow UUTs with tractUse < 1.2\")\n",
    "                    for tt in HDtractList[t]:  #OUT and friends already removed from this list, so their neighbors not checked\n",
    "                        for n in neighborList[tt]:\n",
    "                            if tt not in OUTgroup and n not in addableUUTlist and n not in HDtractList[t] and tractUse[n] < 1.2 :\n",
    "                                addableUUTlist.append(n)\n",
    "                                addableUUTscore[n] = (tractUse[n] - 1.2) * abs(OUT_centerDistance[t]) / tractCP[t].distance(tractCP[n])                \n",
    "                tractToAdd = addableUUTscore.index(np.min(addableUUTscore))\n",
    "                if addableUUTlist == []:\n",
    "                    ohno = input(\"no nearby tracts found with tractUse < 1.2\")\n",
    "                if tractToAdd == 0 :  #should not happen; this is an overused tract\n",
    "                    ohno = input(\"Uh oh, code thinks we should be adding tract 0 which has a score of\",addScore[0])\n",
    "                HDtractList[t].append(tractToAdd)\n",
    "                popToShed -= tractPop[tractToAdd]\n",
    "                popGained += tractPop[tractToAdd]\n",
    "                tractUse[tractToAdd] += tractPop[t] / avgDistrictPop\n",
    "                #print(\"adding tract\",tractToAdd,\"with pop\",tractPop[tractToAdd],\"to HD\",t,\". PoptoShed is now\",popToShed)\n",
    "            if T%nLoopPrint == 0:\n",
    "                print(\"all done exchanging OUTs for UUTs for tract\",t,\". Total popGained =\",popGained,\n",
    "                      \"OUT and t0 usage now\",r3(tractUse[OUT]),r3(tractUse[0]))\n",
    "                            \n",
    "    print(\"we have now reduced\",OUT,\"usage to\",tractUse[OUT],\". Time for next OUT\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "fd844d35-8fed-4442-ab25-07525354def3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will now recalculate all Home District leans\n",
      "I will now capture the patched HDpiece results in a csv file\n"
     ]
    }
   ],
   "source": [
    "print(\"we will now recalculate all Home District leans\")\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts\n",
    "HDarea = [0.]*nTracts\n",
    "HDweight = [0.]*nTracts\n",
    "HDtotVote = [0.]*nTracts\n",
    "\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "    totGOP = 0.   #zero out; the home tract is in the HDtractList\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  \n",
    "    for tt in HDtractList[t]:\n",
    "        fracUse = 1.\n",
    "        if partialTractNo[t] == tt:\n",
    "            fracUse = partialTractUse[t]\n",
    "        totGOP +=   fracUse*vtdTrump[tt]\n",
    "        totVote +=  fracUse*(vtdTrump[tt]+vtdBiden[tt])\n",
    "        totVAP +=   fracUse*tractVAP[tt]\n",
    "        totHisp +=  fracUse*tractHisp[tt]\n",
    "        totBlack += fracUse*tractBlack[tt]\n",
    "        HDarea[t] +=   fracUse*tractArea[tt]\n",
    "        HDvPop[t] +=   fracUse*tractPop[tt]\n",
    "    HDvGOP[t] = totGOP / totVote\n",
    "    HDvBlack[t] = totBlack / totVAP\n",
    "    HDvHisp[t] = totHisp / totVAP\n",
    "    HDtotVote[t] = totVote\n",
    "    \n",
    "\n",
    "print(\"I will now capture the patched HDpiece results in a csv file\")\n",
    "date = \"18Sepfrom14Sep\"\n",
    "tractNo = [0]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractNo[t] = t\n",
    "df = pd.DataFrame( {\"tractNo\":tractNo,\"centroid x\":tractCPx,\"centroid y\":tractCPy,\"tractPop\":tractPop,\"HDwt\":HDweight,\n",
    "                    \"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDtotVote\":HDtotVote,\"HDvBlack\":HDvBlack,\"HDvHisp\":HDvHisp, \"HDarea\":HDarea,\n",
    "                    \"HDtractList\":HDtractList,\"partialNo\":partialTractNo,\"partialUse\":partialTractUse,\n",
    "                   \"vtdBiden\":vtdBiden,\"vtdTrump\":vtdTrump,\"neighborList\":neighborList} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HDpcL\"+date+\"patched.csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "529db728-efe7-4877-a519-b17b05bbd875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I will now compute the tract usage\n",
      "and plot the tract usage distro\n",
      "1.0 0.12521 = avg,std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"I will now compute the tract usage\")\n",
    "tractUse = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    for tt in HDtractList[t]:\n",
    "        tractUse[tt] += tractPop[t] / avgDistrictPop\n",
    "        if partialTractNo[t] == tt:\n",
    "            tractUse[tt] -= tractPop[t] / avgDistrictPop * (1. - partialTractUse[t])\n",
    "print(\"and plot the tract usage distro\")\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * HDweight[t]\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 * HDweight[t]\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "print(r3(avgTractUse),r5(sdTractUse),\"= avg,std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=HDweight)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c6d37b4c-9dbc-46e2-a553-f1a697644b00",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are 0.7 1.6\n",
      "We do not plot tracts with usage between 0.9 and 1.1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are a total of 3 used < 0.7 and 0 used more than 1.6\n"
     ]
    }
   ],
   "source": [
    "minUse = 0.7\n",
    "maxUse = 1.6\n",
    "minNoPlot = 0.9\n",
    "maxNoPlot = 1.1\n",
    "print(\"Here we visualize under-used (black) and over-used (red) tracts. minUse and maxUse for plotting are\",minUse, maxUse)\n",
    "print(\"We do not plot tracts with usage between\",minNoPlot,\"and\",maxNoPlot)\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minNoPlot:\n",
    "        redd = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        gren = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        bluu = min(1, max( 0, (tractUse[t] - minUse)/(minNoPlot - minUse) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy\n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor=(redd,gren,bluu) )\n",
    "    if tractUse[t] > maxNoPlot:\n",
    "        redd = 1\n",
    "        gren = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        bluu = min(1, max( 0, (maxUse - tractUse[t] )/(maxUse - maxNoPlot) ) )\n",
    "        GEOM = tractGeom[t]\n",
    "        if GEOM.type == Polygon([Point(0,0),Point(1,0), Point(1,1)]).type :\n",
    "            EXTERIOR = GEOM.exterior\n",
    "            x,y = EXTERIOR.xy            \n",
    "            plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "        else:\n",
    "            for geom in GEOM.geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.fill(x,y,facecolor= (redd,gren,bluu) )\n",
    "MAPboundary = Polygon([Point(-109,37),Point(-102.06,37),Point(-102.06,41),Point(-109,41)])\n",
    "MAPboundary = MAPboundary.buffer(0.01)\n",
    "plotPoly(MAPboundary)\n",
    "plt.show()\n",
    "nWayOverusedTracts = 0\n",
    "nWayUnderusedTracts = 0\n",
    "for t in range(nTracts):\n",
    "    if tractUse[t] < minUse:\n",
    "        nWayUnderusedTracts +=1\n",
    "    if tractUse[t] > maxUse:\n",
    "        nWayOverusedTracts +=1\n",
    "print(\"there are a total of\",nWayUnderusedTracts,\"used <\",minUse,\"and\",nWayOverusedTracts,\"used more than\",maxUse) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3b324fd6-01b6-438c-9342-3c5dadea3321",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 0.00236 0.43296 HD avg vs true 0.43061\n"
     ]
    },
    {
     "data": {
      "image/png": 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3JsyrIX12FfBjwM8BrwI+m+R4Vf3LlS5u0uYloIYs10SS1wEfBPZW1X9MqbauBvXZt1TVp5N8f5Jrq2oeF/kc0l+7gCOjcLoWuDXJ+ar62FQq7GfNPquqr479fCzJB+b4MwbDl577clV9Dfhakk8Drwc2XUDNyym+NZdcSnID8BHgnZvxN40rYEif/UBG/9uOvqTyamBeg33N/qqqG6tqoaoWgL8C3j3H4QTDPmPfO/YZ283S/1nz+hmDYcvHfRz46SRXJflOlr5B4vSU65yIuRhB1bDlmt4LfDfwgdG/h/M1YysDr8fAPvslltZg/DrwP8Avj02amCsD+0tjBvbZ24FfT3Kepc/Y7fP6GYNhfVZVp5P8NXAS+Cbwwar6wsZVfelc6kiS1NK8nOKTJG0yBpQkqSUDSpLUkgElSWrJgJIktWRASZJaMqAkSS39HyIDp43VKN1wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden))\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "3ec3f408-c94d-4c43-913e-aa1220c5ec1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here is the correlation of HD area to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "... and here is the correlation of tract usage to red-blue lean\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Here is the correlation of HD area to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )   #need to calculate HD area before do\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()\n",
    "print(\"... and here is the correlation of tract usage to red-blue lean\")\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "33362e3b-1cf7-4ddb-a08c-0c5d5f6ba7e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.407 3.13665\n",
      "0.33667 fractional expected GOP seats =  2.693  out of  8 seats\n",
      "simpler expected GOP seats =  2.8555  out of  8 seats\n"
     ]
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "plt.show()\n",
    "\n",
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram\n",
    "\n",
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\", state=\"+str(STATE), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",r3(Rsimple) ,r5(Rsmeared) )   \n",
    "print(r5(smearedSeats),\"fractional expected GOP seats = \",r3(nDistricts*smearedSeats),\" out of \",nDistricts,\"seats\")\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96a159db-98f9-453b-90a6-2f2713e042bf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03f802d8-479a-4fe4-aaec-70363cfd4ec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Let us compute the geog bias and R for the extreme of each precinct = election\")\n",
    "print(\"using 2020 pres as proxy.  Each 'district' awards tractPop / statePop of a seat\")\n",
    "print(\"we compute for -0.02, -0.01, 0, 0.01, 0.02 uniform swings, no fractional analysis\")\n",
    "GOPseats = [0.,0.,0.,0.,0.]\n",
    "swing = [-0.02, -0.01, 0, 0.01, 0.02]\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump)+np.sum(vtdBiden) )\n",
    "for t in range(nTracts):    \n",
    "    for s in range(len(swing)):\n",
    "        seatWeight = tractPop[t] / statePop\n",
    "        if vtdBiden[t] + vtdTrump[t] > 0:  #ignore empty precincts\n",
    "            GOPvote = vtdTrump[t] / (vtdTrump[t] + vtdBiden[t])\n",
    "            adjVote = GOPvote + swing[s]\n",
    "            if adjVote == 0.5:\n",
    "                GOPseats[s] += 0.5 * seatWeight\n",
    "            elif adjVote > 0.5:\n",
    "                GOPseats[s] += seatWeight\n",
    "print(\"for swings of\",swing,\"GOP seat fractions were\",GOPseats,\"stateVote was\",stateGOP)"
   ]
  }
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